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Wei Y, Palazzolo L, Ben Mariem O, Bianchi D, Laurenzi T, Guerrini U, Eberini I. Investigation of in silico studies for cytochrome P450 isoforms specificity. Comput Struct Biotechnol J 2024; 23:3090-3103. [PMID: 39188968 PMCID: PMC11347072 DOI: 10.1016/j.csbj.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/28/2024] Open
Abstract
Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.
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Affiliation(s)
- Yao Wei
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Luca Palazzolo
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Omar Ben Mariem
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Davide Bianchi
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Tommaso Laurenzi
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Uliano Guerrini
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
| | - Ivano Eberini
- Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, Via Giuseppe Balzaretti 9, 20133 Milano, Italy
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Yang Y, Yang Z, Pang X, Cao H, Sun Y, Wang L, Zhou Z, Wang P, Liang Y, Wang Y. Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 953:176095. [PMID: 39245376 DOI: 10.1016/j.scitotenv.2024.176095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 09/03/2024] [Accepted: 09/04/2024] [Indexed: 09/10/2024]
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.
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Affiliation(s)
- Ying Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zeguo Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Xudi Pang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Huiming Cao
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
| | - Yuzhen Sun
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ling Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zhen Zhou
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Pu Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
| | - Yawei Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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3
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Chebotaev PP, Buglak AA, Sheehan A, Filatov MA. Predicting fluorescence to singlet oxygen generation quantum yield ratio for BODIPY dyes using QSPR and machine learning. Phys Chem Chem Phys 2024; 26:25131-25142. [PMID: 39311461 DOI: 10.1039/d4cp02471k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
Functional dyes that are capable of both bright fluorescence and efficient singlet oxygen generation are crucial for theranostic techniques, which integrate fluorescence imaging and photodynamic therapy (PDT). The development of new functional dyes for theranostics is often costly and time-consuming due to laborious synthesis and post-synthetic screening of large libraries of compounds. In this work, we describe machine learning methods suitable for simultaneous prediction of fluorescence and photosensitizing ability of heavy-atom-free boron dipyrromethene (BODIPY) compounds. We analysed the ratio between fluorescence quantum yield (ΦFl) and singlet oxygen quantum yield (ΦΔ) for over 70 BODIPY structures in polar (acetonitrile) and non-polar (toluene) solvents, which mimic hydrophilic and hydrophobic cell environments, respectively. QSPR models were developed based on more than 5000 calculated molecular descriptors, including quantum chemical and topological descriptors. We applied multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR) methods for model building and optimization. The resulting models demonstrated robust statistical parameters (R2 = 0.73-0.91) for both polar and non-polar media. The relative contributions of the descriptors to the models were assessed, identifying Eig03_EA(dm), F01[C-N], and TDB06p as the most influential. These results demonstrate that QSPR machine learning methods are effective in predicting key photochemical parameters of BODIPY photosensitizers, thereby potentially streamlining the development of theranostic agents.
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Affiliation(s)
- Platon P Chebotaev
- Faculty of Physics, Saint-Petersburg State University, Universiteteskaya Emb. 7-9, 199034 St. Petersburg, Russia.
| | - Andrey A Buglak
- Faculty of Physics, Saint-Petersburg State University, Universiteteskaya Emb. 7-9, 199034 St. Petersburg, Russia.
- Institute of Physics, Kazan Federal University, 18 Kremlyovskaya street, 420008, Kazan, Russia
| | - Aimee Sheehan
- School of Chemical and Biopharmaceutical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Mikhail A Filatov
- School of Chemical and Biopharmaceutical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
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4
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Meng J, Zhang L, He Z, Hu M, Liu J, Bao W, Tian Q, Feng H, Liu H. Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors. J Comput Chem 2024. [PMID: 39325045 DOI: 10.1002/jcc.27510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/21/2024] [Accepted: 09/11/2024] [Indexed: 09/27/2024]
Abstract
Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-specific scoring function for hDHODH (TSSF-hDHODH). The Pearson correlation coefficient values of TSSF-hDHODH in the cross-validation and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random forest (RF) based generic scoring function RF-Score. TSSF-hDHODH is further used for the virtual screening of potential inhibitors in the FDA-Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynamics simulations, crizotinib is identified as a candidate for subsequent structural optimization. This study can be useful for the discovery of hDHODH inhibitors and the development of scoring functions for additional targets.
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Affiliation(s)
- Jinhui Meng
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
- Liaoning Provincial Key Laboratory of Computational Simulation and Information Processing of Biomacromolecules, Liaoning University, Shenyang, Liaoning, China
- Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Liaoning University, Shenyang, Liaoning, China
| | - Zhe He
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
| | - Mengfeng Hu
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
| | - Jinhan Liu
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
| | - Wenzhuo Bao
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
| | - Qifeng Tian
- School of Life Science, Liaoning University, Shenyang, Liaoning, China
| | - Huawei Feng
- School of Pharmacy, Liaoning University, Shenyang, Liaoning, China
| | - Hongsheng Liu
- Liaoning Provincial Key Laboratory of Computational Simulation and Information Processing of Biomacromolecules, Liaoning University, Shenyang, Liaoning, China
- Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Liaoning University, Shenyang, Liaoning, China
- School of Pharmacy, Liaoning University, Shenyang, Liaoning, China
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5
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McCormick LA, McCormick JW, Park C, Follit CA, Wise JG, Vogel PD. Computationally accelerated identification of P-glycoprotein inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583428. [PMID: 39345515 PMCID: PMC11430104 DOI: 10.1101/2024.03.05.583428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Overexpression of the polyspecific efflux transporter, P-glycoprotein (P-gp, MDR1, ABCB1 ), is a major mechanism by which cancer cells acquire multidrug resistance (MDR), the resistance to diverse chemotherapeutic drugs. Inhibiting drug transport by P-gp can resensitize cancer cells to chemotherapy, but there are no P-gp inhibitors available to patients. Clinically unsuccessful P-gp inhibitors tend to bind at the pump's transmembrane drug binding domains and are often P-gp transport substrates, resulting in lowered intracellular concentration of the drug and altered pharmacokinetics. In prior work, we used computationally accelerated drug discovery to identify novel P-gp inhibitors that target the pump's cytoplasmic nucleotide binding domains. Our first-draft study provided conclusive evidence that the nucleotide binding domains of P-gp are viable targets for drug discovery. Here we develop an enhanced, computationally accelerated drug discovery pipeline that expands upon our prior work by iteratively screening compounds against multiple conformations of P-gp with molecular docking. Targeted molecular dynamics simulations with our homology model of human P-gp were used to generate docking receptors in conformations mimicking a putative drug transport cycle. We offset the increased computational complexity using custom Tanimoto chemical datasets, which maximize the chemical diversity of ligands screened by docking. Using our expanded, virtual-assisted pipeline, we identified nine novel P-gp inhibitors that reverse MDR in two types of P-gp overexpressing human cancer cell lines, reflecting a 13.4% hit rate. Of these inhibitors, all were non-toxic to non-cancerous human cells, and six were not likely to be transport substrates of P-gp. Our novel P-gp inhibitors are chemically diverse and are good candidates for lead optimization. Our results demonstrate that the nucleotide binding domains of P-gp are an underappreciated target in the effort to reverse P-gp-mediated multidrug resistance in cancer.
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6
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Fang J, Tang Y, Gong C, Huang Z, Feng Y, Liu G, Tang Y, Li W. Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models. Chem Res Toxicol 2024; 37:1535-1548. [PMID: 39196814 DOI: 10.1021/acs.chemrestox.4c00199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2024]
Abstract
Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the clinically used drugs. P450-mediated metabolism is also closely associated with the formation of toxic metabolites and drug-drug interactions. Therefore, it is of high importance to predict if a compound is the substrate of a given P450 in the early stage of drug development. In this study, we built the multitask learning models to simultaneously predict the substrates of five major drug-metabolizing P450 enzymes, namely, CYP3A4, 2C9, 2C19, 2D6, and 1A2, based on the collected substrate data sets. Compared to the single-task model and conventional machine learning models, the multitask fingerprints and graph neural networks model achieved superior performance with the average AUC values of 90.8% on the test set. Notably, the multitask model demonstrated its good performance on the small amount of substrate data sets such as CYP1A2, 2C9, and 2C19. In addition, the Shapley additive explanation and the attention mechanism were used to reveal specific substructures associated with P450 substrates, which were further confirmed and complemented by the substructure mining tool and the literature.
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Affiliation(s)
- Jiaojiao Fang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yan Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Changda Gong
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zejun Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yanjun Feng
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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7
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Eytcheson SA, Zosel AD, Olker JH, Hornung MW, Degitz SJ. In Vitro Screening for ToxCast Chemicals Binding to Thyroxine-Binding Globulin. Chem Res Toxicol 2024. [PMID: 39268642 DOI: 10.1021/acs.chemrestox.4c00183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2024]
Abstract
Thyroid hormone (TH) carrier proteins play an important role in distributing TH to target tissue as well as maintaining the balance of free versus bound TH in the blood. Interference with the TH carrier proteins has been identified as a potential mechanism of thyroid system disruption. To address the lack of data regarding chemicals binding to these carrier proteins and displacing TH, a fluorescence-based in vitro screening assay was utilized to screen over 1,400 chemicals from the U.S. EPA's ToxCast phase1_v2, phase 2, and e1k libraries for competitive binding to one of the carrier proteins, thyroxine-binding globulin. Initial screening at a single high concentration of 100 μM identified 714 chemicals that decreased signal of the bound fluorescent ligand by 20% or higher. Of these, 297 produced 50% or greater reduction in fluorescence and were further tested in concentration-response (0.004 to 150 μM) to determine relative potency. Ten chemicals were found to have EC50 values <1 μM, 63 < 10 μM, and 141 chemicals between 10 and 100 μM. Utilization of this assay contributes to expanding the number of in vitro assays available for identifying chemicals with the potential to disrupt TH homeostasis. These results support ranking and prioritization of chemicals to be tested in vivo to aid in the development of a framework for predicting in vivo effects from in vitro high-throughput data.
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Affiliation(s)
- Stephanie A Eytcheson
- Oak Ridge Institute for Science and Education Postdoctoral Fellow, Oak Ridge, Tennessee 37830, United States
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota 55804, United States
| | - Alexander D Zosel
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota 55804, United States
- Oak Ridge Associated Universities Student Services Contractor, Oak Ridge, Tennessee 37830, United States
| | - Jennifer H Olker
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota 55804, United States
| | - Michael W Hornung
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota 55804, United States
| | - Sigmund J Degitz
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota 55804, United States
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8
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Eytcheson SA, Zosel AD, Olker JH, Hornung MW, Degitz SJ. Screening the ToxCast Chemical Libraries for Binding to Transthyretin. Chem Res Toxicol 2024. [PMID: 39258767 DOI: 10.1021/acs.chemrestox.4c00215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Transthyretin (TTR) is one of the serum binding proteins responsible for transport of thyroid hormones (TH) to target tissue and for maintaining the balance of available TH. Chemical binding to TTR and subsequent displacement of TH has been identified as an end point in screening chemicals for potential disruption of the thyroid system. To address the lack of data regarding chemicals binding to TTR, we optimized an in vitro assay utilizing the fluorescent probe 8-anilino-1-napthalenesulfonic acid (ANSA) and the human protein TTR to screen over 1500 chemicals from the U.S. EPA's ToxCast ph1_v2, ph2, and e1k libraries utilizing a tiered approach. Testing of a single high concentration (target 100 μM) resulted in 888 chemicals with 20% or greater activity based on displacement of ANSA from TTR. Of these, 282 chemicals had activity of 85% or greater and were further tested in 12-point concentration-response with target concentrations ranging from 0.015 to 100 μM. An EC50 was obtained for 276 of these 301 chemicals. To date, this is the largest set of chemicals screened for binding to TTR. Utilization of this assay is a significant contribution toward expanding the suite of in vitro assays used to identify chemicals with the potential to disrupt thyroid hormone homeostasis.
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Affiliation(s)
- Stephanie A Eytcheson
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37830, United States
- Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, U.S. Environmental Protection Agency, Office of Research and Development, Duluth, Minnesota 55804, United States
| | - Alexander D Zosel
- Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, U.S. Environmental Protection Agency, Office of Research and Development, Duluth, Minnesota 55804, United States
- Oak Ridge Associated Universities Student Services Contractor, Oak Ridge, Tennessee 37830, United States
| | - Jennifer H Olker
- Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, U.S. Environmental Protection Agency, Office of Research and Development, Duluth, Minnesota 55804, United States
| | - Michael W Hornung
- Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, U.S. Environmental Protection Agency, Office of Research and Development, Duluth, Minnesota 55804, United States
| | - Sigmund J Degitz
- Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, U.S. Environmental Protection Agency, Office of Research and Development, Duluth, Minnesota 55804, United States
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Noga M, Jurowski K. Toxicity of Bromo-DragonFLY as a New Psychoactive Substance: Application of In Silico Methods for the Prediction of Key Toxicological Parameters Important to Clinical and Forensic Toxicology. Chem Res Toxicol 2024. [PMID: 39119730 DOI: 10.1021/acs.chemrestox.4c00105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Bromo-DragonFLY is a synthetic new psychoactive substance (NPS) that has gained attention due to its powerful and long-lasting hallucinogenic effects, legal status, and widespread availability. This study aimed to use various in silico toxicology methods to predict key toxicological parameters for Bromo-DragonFLY, including acute toxicity (LD50), genotoxicity, cardiotoxicity, health effects, and the potential for endocrine disruption. The results indicate significant acute toxicity with noticeable variations across different species, a low likelihood of genotoxic potential suggesting potential DNA damage, and a notable risk of cardiotoxicity associated with inhibition of the hERG channel. Evaluation of endocrine disruption suggests a low probability of Bromo-DragonFLY interacting with the estrogen receptor α (ER-α), indicating minimal estrogenic activity. These insights from in silico investigations are important for advancing our understanding of this NPS in forensic and clinical toxicology. These initial toxicological examinations establish a foundation for future research efforts and contribute to developing risk assessment and management strategies for using and misusing NPS.
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Affiliation(s)
- Maciej Noga
- Department of Regulatory and Forensic Toxicology, Institute of Medical Expertises in Łódź, Ul. Aleksandrowska 67/93, 91-205 Łódź, Poland
| | - Kamil Jurowski
- Department of Regulatory and Forensic Toxicology, Institute of Medical Expertises in Łódź, Ul. Aleksandrowska 67/93, 91-205 Łódź, Poland
- Laboratory of Innovative Toxicological Research and Analyzes, Institute of Medical Studies, Medical College, Rzeszów University, Al. Mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
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10
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Hodyna D, Klipkov A, Kachaeva M, Shulha Y, Gerus I, Metelytsia L, Kovalishyn V. In Silico Design and In Vitro Assessment of Bicyclic Trifluoromethylated Pyrroles as New Antibacterial and Antifungal Agents. Chem Biodivers 2024; 21:e202400638. [PMID: 38837284 DOI: 10.1002/cbdv.202400638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/04/2024] [Indexed: 06/07/2024]
Abstract
QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q2=0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q2=0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated in vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1H, 19F, and 13C NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39 mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis.
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Affiliation(s)
- Diana Hodyna
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
| | - Anton Klipkov
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
- National University of Kyiv -, Mohyla Academy, 2, Skovorody Str., Kyiv, 04070, Ukraine
| | - Maryna Kachaeva
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
| | - Yurii Shulha
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
| | - Igor Gerus
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
| | - Larysa Metelytsia
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
| | - Vasyl Kovalishyn
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 1 Academician Kukhar Str., Kyiv, 02094, Ukraine
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11
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Zhao X, Kong Y, Ji Y, Xin X, Chen L, Chen G, Yu C. Classification models for predicting the bioactivity of pan-TRK inhibitors and SAR analysis. Mol Divers 2024; 28:2077-2097. [PMID: 37910346 DOI: 10.1007/s11030-023-10735-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/22/2023] [Indexed: 11/03/2023]
Abstract
Tropomyosin receptor kinases (TRKs) are important broad-spectrum anticancer targets. The oncogenic rearrangement of the NTRK gene disrupts the extracellular structural domain and epitopes for therapeutic antibodies, making small-molecule inhibitors essential for treating NTRK fusion-driven tumors. In this work, several algorithms were used to construct descriptor-based and nondescriptor-based models, and the models were evaluated by outer 10-fold cross-validation. To find a model with good generalization ability, the dataset was partitioned by random and cluster-splitting methods to construct in- and cross-domain models, respectively. Among the 48 models built, the model with the combination of the deep neural network (DNN) algorithm and extended connectivity fingerprints 4 (ECFP4) descriptors achieved excellent performance in both dataset divisions. The results indicate that the DNN algorithm has a strong generalization prediction ability, and the richness of features plays a vital role in predicting unknown spatial molecules. Additionally, we combined the clustering results and decision tree models of fingerprint descriptors to perform structure-activity relationship analysis. It was found that nitrogen-containing aromatic heterocyclic and benzo heterocyclic structures play a crucial role in enhancing the activity of TRK inhibitors.
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Affiliation(s)
- Xiaoman Zhao
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
- College of Bio engineering, No. 9 Liangshuihe 1st Street, Beijing, 100176, People's Republic of China
| | - Yue Kong
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
| | - Yueshan Ji
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
| | - Xiulan Xin
- College of Bio engineering, No. 9 Liangshuihe 1st Street, Beijing, 100176, People's Republic of China
| | - Liang Chen
- College of Bio engineering, No. 9 Liangshuihe 1st Street, Beijing, 100176, People's Republic of China
| | - Guang Chen
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China
| | - Changyuan Yu
- College of Life Science and Technology, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing, 100029, People's Republic of China.
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12
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Mervin L, Voronov A, Kabeshov M, Engkvist O. QSARtuna: An Automated QSAR Modeling Platform for Molecular Property Prediction in Drug Design. J Chem Inf Model 2024; 64:5365-5374. [PMID: 38950185 DOI: 10.1021/acs.jcim.4c00457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Machine-learning (ML) and deep-learning (DL) approaches to predict the molecular properties of small molecules are increasingly deployed within the design-make-test-analyze (DMTA) drug design cycle to predict molecular properties of interest. Despite this uptake, there are only a few automated packages to aid their development and deployment that also support uncertainty estimation, model explainability, and other key aspects of model usage. This represents a key unmet need within the field, and the large number of molecular representations and algorithms (and associated parameters) means it is nontrivial to robustly optimize, evaluate, reproduce, and deploy models. Here, we present QSARtuna, a molecule property prediction modeling pipeline, written in Python and utilizing the Optuna, Scikit-learn, RDKit, and ChemProp packages, which enables the efficient and automated comparison between molecular representations and machine learning models. The platform was developed by considering the increasingly important aspect of model uncertainty quantification and explainability by design. We provide details for our framework and provide illustrative examples to demonstrate the capability of the software when applied to simple molecular property, reaction/reactivity prediction, and DNA encoded library enrichment classification. We hope that the release of QSARtuna will further spur innovation in automatic ML modeling and provide a platform for education of best practices in molecular property modeling. The code for the QSARtuna framework is made freely available via GitHub.
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Affiliation(s)
- Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, United Kingdom
| | - Alexey Voronov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Mikhail Kabeshov
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, University of Gothenburg, Chalmers University of Technology, Gothenburg 412 96, Sweden
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13
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Fu L, Shi S, Yi J, Wang N, He Y, Wu Z, Peng J, Deng Y, Wang W, Wu C, Lyu A, Zeng X, Zhao W, Hou T, Cao D. ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res 2024; 52:W422-W431. [PMID: 38572755 PMCID: PMC11223840 DOI: 10.1093/nar/gkae236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/10/2024] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.
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Affiliation(s)
- Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P.R. China
| | - Shaohua Shi
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR, 999077, P.R. China
| | - Jiacai Yi
- School of Computer Science, National University of Defense Technology, Changsha, Hunan 410073, P.R. China
| | - Ningning Wang
- Xiangya Hospital of Central South University, Changsha, Hunan 410008, P.R. China
| | - Yuanhang He
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P.R. China
| | - Zhenxing Wu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P.R. China
| | - Jinfu Peng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P.R. China
| | - Youchao Deng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P.R. China
| | - Wenxuan Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P.R. China
| | - Chengkun Wu
- School of Computer Science, National University of Defense Technology, Changsha, Hunan 410073, P.R. China
| | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR, 999077, P.R. China
| | - Xiangxiang Zeng
- Department of Computer Science, Hunan University, Changsha, Hunan 410082, P.R. China
| | - Wentao Zhao
- School of Computer Science, National University of Defense Technology, Changsha, Hunan 410073, P.R. China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P.R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, P.R. China
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14
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Dhanalakshmi M, Sruthi D, Das K, Iqbal M, Mohanan VP, Dave S, Muthulakshmi Andal N. Graph theoretical descriptors differentiate d-Mannose isomers in the principal component proposed feature space: A computational approach. Carbohydr Res 2024; 541:109147. [PMID: 38781716 DOI: 10.1016/j.carres.2024.109147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 05/06/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
The intricate nature of carbohydrates, particularly monosaccharides, stems from the existence of several chiral centers within their tertiary structures. Predicting and characterizing the molecular geometries and electrostatic landscapes of these substances is difficult due to their complex electrical properties. Moreover, these structures can display a substantial degree of conformational flexibility due to the presence of many rotatable bonds. Moreover, identifying and distinguishing between D and L enantiomers of monosaccharides presents a significant analytical obstacle since there is a need for empirically measurable properties that can distinguish them. This work uses Principal Component Analysis (PCA) to explore the chemical information included in 3D descriptors in order to comprehend the conformational space of d-Mannose stereoisomers. The isomers may be discriminated by utilizing 3D matrix-based indices, geometrical descriptors, and RDF descriptors. The isomers can be distinguished by descriptors, such as the Harary-like index from the reciprocal squared geometrical matrix (H_RG), Harary-like index from Coulomb matrix (H_Coulomb), Wiener-like index from Coulomb matrix (Wi_Coulomb), Wiener-like index from geometrical matrix (Wi_G), Graph energy from Coulomb matrix (SpAbs_Coulomb), Spectral absolute deviation from Coulomb matrix (SpAD_Coulomb), and Spectral positive sum from Coulomb matrix (SpPos_Coulomb). Among these descriptors, the first two, H_RG and H_Coulomb, perform the best in differentiation among the 3D-Matrix-Based Descriptors (3D-MBD) class. The results obtained from this study provide a significant chemical insight into the structural characteristics of the compounds inside the graph theoretical framework. These findings are likely to serve as the basis for developing new methods for analytical experiments.
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Affiliation(s)
- M Dhanalakshmi
- Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India
| | - D Sruthi
- Department of Biochemistry, Indian Institute of Science, Bengaluru, Karnataka, India
| | - Kajari Das
- Department of Biotechnology, College of Basic Science and Humanities, Odisha University of Agriculture and Technology, Bhubaneswar-3, Odisha, India
| | - Muhammed Iqbal
- Department of Chemistry, University of Calicut, Kerala, India
| | - V P Mohanan
- Department of Chemistry, University of Calicut, Kerala, India
| | - Sushma Dave
- Department of Chemistry, JIET, Jodhpur, Rajasthan, India.
| | - N Muthulakshmi Andal
- Department of Chemistry, PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, India.
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15
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Bandini E, Castellano Ontiveros R, Kajtazi A, Eghbali H, Lynen F. Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms. J Cheminform 2024; 16:72. [PMID: 38907264 PMCID: PMC11193285 DOI: 10.1186/s13321-024-00873-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 06/14/2024] [Indexed: 06/23/2024] Open
Abstract
Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At 45 ∘ C , logP predominantly influenced retention, akin to reversed-phase columns, while at5 ∘ C , complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.
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Affiliation(s)
- Elena Bandini
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Univeristy of Ghent, Krijgslaan 281 S4bis, Ghent, 9000, Belgium.
| | - Rodrigo Castellano Ontiveros
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, 11428, Sweden
| | - Ardiana Kajtazi
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Univeristy of Ghent, Krijgslaan 281 S4bis, Ghent, 9000, Belgium
| | - Hamed Eghbali
- Packaging and Specialty Plastics R&D, Dow Benelux B.V., Terneuzen, 4530 AA, the Netherlands
| | - Frédéric Lynen
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Univeristy of Ghent, Krijgslaan 281 S4bis, Ghent, 9000, Belgium
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16
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Sarfraz M, Ayyaz M, Rauf A, Yaqoob A, Tooba, Arif Ali M, Siddique SA, Qureshi AM, Sarfraz MH, Aljowaie RM, Almutairi SM, Arshad M. New Pyrimidinone Bearing Aminomethylenes and Schiff Bases as Potent Antioxidant, Antibacterial, SARS-CoV-2, and COVID-19 Main Protease M Pro Inhibitors: Design, Synthesis, Bioactivities, and Computational Studies. ACS OMEGA 2024; 9:25730-25747. [PMID: 38911743 PMCID: PMC11191110 DOI: 10.1021/acsomega.3c09393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/25/2024]
Abstract
New 2-thioxopyrimidinone derivatives (A1-A10) were synthesized in 87-96% yields via a simple three-component condensation reaction. These compounds were screened extensively through in vitro assays for antioxidant and antibacterial investigations. The DPPH assays resulted in the excellent potency of A6-A10 as antioxidants with IC50 values of 0.83 ± 0.125, 0.90 ± 0.77, 0.36 ± 0.063, 1.4 ± 0.07, and 1.18 ± 0.06 mg/mL, which were much better than 1.79 ± 0.045 mg/mL for the reference ascorbic acid. These compounds exhibited better antibacterial potency against Klebsiella with IC50 values of 2 ± 7, 1.32 ± 8.9, 1.19 ± 11, 1.1 ± 12, and 1.16 ± 11 mg/mL for A6-A10. High-throughput screenings (HTS) of these motifs were carried out including investigation of drug-like behaviors, physiochemical property evaluation, and structure-related studies involving DFT and metabolic transformation trends. The radical scavenging ability of the synthesized motifs was validated through molecular docking studies through ligand-protein binding against human inducible nitric oxide synthase (HINOS) PDB ID: 4NOS, and the results were promising. Furthermore, the antiviral capability of the compounds was examined by in silico studies using two viral proteins PDB ID: 6Y84 and PDB ID: 6LU7. Binding poses of ligands were discussed, and amino acids in the protein binding pockets were investigated, where the tested compounds showed much better binding affinities than the standard inhibitors, proving to be suitable leads for antiviral drug discovery. The stabilities of the molecular docked complexes in real systems were validated by molecular dynamics simulations.
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Affiliation(s)
- Muhammad Sarfraz
- Institute
of Chemistry, The Islamia University of
Bahawalpur, Bahawalpur 63100, Pakistan
| | - Muhammad Ayyaz
- Institute
of Chemistry, The Islamia University of
Bahawalpur, Bahawalpur 63100, Pakistan
| | - Abdul Rauf
- Institute
of Chemistry, The Islamia University of
Bahawalpur, Bahawalpur 63100, Pakistan
| | - Asma Yaqoob
- Institute
of Biochemistry, Biotechnology, and Bioinformatics. Department of
Biochemistry and Molecular Biology, The
Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
| | - Tooba
- Institute
of Biochemistry, Biotechnology, and Bioinformatics. Department of
Biochemistry and Molecular Biology, The
Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
| | - Muhammad Arif Ali
- Institute
of Chemistry, The Islamia University of
Bahawalpur, Bahawalpur 63100, Pakistan
| | - Sabir Ali Siddique
- Institute
of Chemistry, The Islamia University of
Bahawalpur, Bahawalpur 63100, Pakistan
| | - Ashfaq Mahmood Qureshi
- Department
of Chemistry, Government Sadiq College Women
University, Bahawalpur 63100, Pakistan
| | - Muhammad Hassan Sarfraz
- Nuffield
Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences,
Botnar Institute of Musculoskeletal Sciences, University of Oxford, OxfordOX3 7LD, United
Kingdom
| | - Reem M. Aljowaie
- Department
of Botany and Microbiology, College of Science, King Saud University, P O 2455 Riyadh 11451, Saudi Arabia
| | - Saeedah Musaed Almutairi
- Department
of Botany and Microbiology, College of Science, King Saud University, P O 2455 Riyadh 11451, Saudi Arabia
| | - Muhammad Arshad
- Institute
of Chemistry, The Islamia University of
Bahawalpur, Bahawalpur 63100, Pakistan
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17
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Yin Y, Hu H, Yang J, Ye C, Goh WWB, Kong AWK, Wu J. OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs. Bioinformatics 2024; 40:btae365. [PMID: 38889277 PMCID: PMC11208724 DOI: 10.1093/bioinformatics/btae365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 05/14/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.
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Affiliation(s)
- Yueming Yin
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- College of Computing and Data Science, Nanyang Technological University, 639798, Singapore
| | - Haifeng Hu
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Jitao Yang
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Chun Ye
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, 637551, Singapore
- School of Biological Sciences, Nanyang Technological University, 637551, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, 637551, Singapore
- Center for AI in Medicine, Nanyang Technological University, 639798, Singapore
- Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London W12 0NN, U.K
| | - Adams Wai-Kin Kong
- College of Computing and Data Science, Nanyang Technological University, 639798, Singapore
| | - Jiansheng Wu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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18
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Jurowski K, Niżnik Ł. Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes. Int J Mol Sci 2024; 25:5867. [PMID: 38892053 PMCID: PMC11173054 DOI: 10.3390/ijms25115867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/23/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
This study reports the first application of in silico methods to assess the toxicity of 4-chloromethcathinone (4-CMC), a novel psychoactive substance (NPS). Employing advanced toxicology in silico tools, it was possible to predict crucial aspects of the toxicological profile of 4-CMC, including acute toxicity (LD50), genotoxicity, cardiotoxicity, and its potential for endocrine disruption. The obtained results indicate significant acute toxicity with species-specific variability, moderate genotoxic potential suggesting the risk of DNA damage, and a notable cardiotoxicity risk associated with hERG channel inhibition. Endocrine disruption assessment revealed a low probability of 4-CMC interacting with estrogen receptor alpha (ER-α), suggesting minimal estrogenic activity. These insights, derived from in silico studies, are critical in advancing the understanding of 4-CMC properties in forensic and clinical toxicology. These initial toxicological findings provide a foundation for future research and aid in the formulation of risk assessment and management strategies in the context of the use and abuse of NPSs.
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Affiliation(s)
- Kamil Jurowski
- Laboratory of Innovative Toxicological Research and Analyses, Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
- Department of Regulatory and Forensic Toxicology, Institute of Medical Expertise, Łódź, ul. Aleksandrowska 67/93, 91-205 Łódź, Poland;
| | - Łukasz Niżnik
- Department of Regulatory and Forensic Toxicology, Institute of Medical Expertise, Łódź, ul. Aleksandrowska 67/93, 91-205 Łódź, Poland;
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19
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Yang Z, Huang T, Pan L, Wang J, Wang L, Ding J, Xiao J. QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning. J Cheminform 2024; 16:48. [PMID: 38685101 PMCID: PMC11059686 DOI: 10.1186/s13321-024-00843-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/24/2024] [Indexed: 05/02/2024] Open
Abstract
Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .
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Affiliation(s)
- Zhijiang Yang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Tengxin Huang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Li Pan
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Jingjing Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
| | - Junhua Xiao
- State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.
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20
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Long TZ, Jiang DJ, Shi SH, Deng YC, Wang WX, Cao DS. Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence. J Chem Inf Model 2024; 64:3222-3236. [PMID: 38498003 DOI: 10.1021/acs.jcim.4c00159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.
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Affiliation(s)
- Teng-Zhi Long
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - De-Jun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Shao-Hua Shi
- Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
| | - You-Chao Deng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Wen-Xuan Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China
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21
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Lin J, He Y, Ru C, Long W, Li M, Wen Z. Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation. Int J Mol Sci 2024; 25:4516. [PMID: 38674100 PMCID: PMC11050562 DOI: 10.3390/ijms25084516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.
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Affiliation(s)
- Jinzhu Lin
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yujie He
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Chengxiang Ru
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Wulin Long
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Zhining Wen
- College of Chemistry, Sichuan University, Chengdu 610064, China
- Medical Big Data Center, Sichuan University, Chengdu 610064, China
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22
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Shkil DO, Muhamedzhanova AA, Petrov PI, Skorb EV, Aliev TA, Steshin IS, Tumanov AV, Kislinskiy AS, Fedorov MV. Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation. Molecules 2024; 29:1826. [PMID: 38675645 PMCID: PMC11055041 DOI: 10.3390/molecules29081826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.
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Affiliation(s)
- Dmitrii O. Shkil
- Syntelly LLC, Moscow 121205, Russia; (A.A.M.); (I.S.S.); (A.V.T.); (A.S.K.)
- Moscow Institute of Physics and Technology, Moscow 141700, Russia
| | | | | | - Ekaterina V. Skorb
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg 191002, Russia; (E.V.S.); (T.A.A.)
| | - Timur A. Aliev
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg 191002, Russia; (E.V.S.); (T.A.A.)
| | - Ilya S. Steshin
- Syntelly LLC, Moscow 121205, Russia; (A.A.M.); (I.S.S.); (A.V.T.); (A.S.K.)
| | | | | | - Maxim V. Fedorov
- Kharkevich Institute for Information Transmission Problems of Russian Academy of Sciences, Moscow 127994, Russia
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23
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Comelli NC, Diez PA, Rodríguez MR, Denett GO, López TE, Bracamonte DM, Ortiz EV, Sampietro DA, Duchowicz PR. Excito-repellent and Pesticide-Likeness Properties of Essential Oils on Carpophilus dimidiatus (Fabricius) (Nitidulidae) and Oryzaephilus mercator (L.) (Silvanidae). J Chem Inf Model 2024; 64:2467-2487. [PMID: 37774492 DOI: 10.1021/acs.jcim.3c01198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2023]
Abstract
Carpophilus dimidiatus (Fabricius) (Nitidulidae) and Oryzaephilus mercator (L.) (Silvanidae) are insect pests that cause severe damage in important walnut growing regions in the northwest of Argentina. The current management approaches for these pests involve the use of unsafe phosphorus pesticides whose overuse have led to farmworker poisoning, pest resistance issues, and environmental contamination. Plant extracts, particularly essential oils, are an alternative source of insect control agents. Excito-repellent essential oils can be used to develop ecofriendly tools for managing the pest population without affecting quality and visual appearance of the stored walnuts. Laboratory studies were conducted to assess the excito-repellent effects of C. dimidiatus and O. mercator of 12 essential oils derived from aromatic plants used as food additives and traditional medicine in Argentina: Aloysia citrodora (AC), Aloysia gratissima (AG), Aloysia gratissima var. Gratissima (AGG), Blepharocalyx salicipholius (BS), Hyptis mutabilis (HM), Lippia junelliana (LJ), Lippia turbinata (LT), Mentha x piperita (MP), Minthostachys mollis (MM), Minthostachys verticillata (MV), Origanum vulgare(OV), and Rosmarinus officinalis (RO). The most bioactive EOs (ERijk ≥ 70%) were Aloysia gratissima var. Gratissima (AGG), Minthostachys verticillata, and Lippia junelliana. Their bioactivity profile and chemical space, characterized from GC-MS measures, Generalized Estimating Equations, and Hierarchical Cluster Analysis, revealed that they are mixtures of very functionalized molecules with physicochemical properties similar to those of insecticides with low residual property that enter the insect body through the respiratory system by inhalation. The AGG, MV, and LJ oils are promising as protective agents of walnut products. In our laboratory, studies of their formulations for use in integrated pest management programs are still ongoing.
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Affiliation(s)
- Nieves C Comelli
- Laboratorio de Control Biológico y Biodiversidad de Insectos (LACBBI), Centro Regional de Energía y Ambiente para el Desarrollo Sustentable (CREAS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Catamarca (UNCA), Prado 366, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
- Facultad de Ciencias Agrarías, Universidad Nacional de Catamarca (UNCA), Máximo Victoria 50, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
| | - Patricia A Diez
- Laboratorio de Control Biológico y Biodiversidad de Insectos (LACBBI), Centro Regional de Energía y Ambiente para el Desarrollo Sustentable (CREAS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Catamarca (UNCA), Prado 366, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
- Departamento Básicas y Tecnológicas, UNdeC, 5360 Chilecito, La Rioja, Argentina
| | - María R Rodríguez
- Laboratorio de Control Biológico y Biodiversidad de Insectos (LACBBI), Centro Regional de Energía y Ambiente para el Desarrollo Sustentable (CREAS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Catamarca (UNCA), Prado 366, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
- Facultad de Tecnología y Ciencias Aplicadas, CONICET-Universidad Nacional de Catamarca (UNCA), K4700BDH, Catamarca, Argentina
| | - Gabriel O Denett
- Laboratorio de Control Biológico y Biodiversidad de Insectos (LACBBI), Centro Regional de Energía y Ambiente para el Desarrollo Sustentable (CREAS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Catamarca (UNCA), Prado 366, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
- Facultad de Ciencias Agrarías, Universidad Nacional de Catamarca (UNCA), Máximo Victoria 50, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
| | - Tamara E López
- Facultad de Ciencias Agrarías, Universidad Nacional de Catamarca (UNCA), Máximo Victoria 50, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
| | - Daniela M Bracamonte
- Facultad de Ciencias Agrarías, Universidad Nacional de Catamarca (UNCA), Máximo Victoria 50, K4700BDH, San Fernando Del Valle de Catamarca, Argentina
| | - Erlinda V Ortiz
- Facultad de Tecnología y Ciencias Aplicadas, CONICET-Universidad Nacional de Catamarca (UNCA), K4700BDH, Catamarca, Argentina
| | - Diego A Sampietro
- Laboratorio de Biología de Agentes Bioactivos y Fitopatógenos (LABIFITO), Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán, Ayacucho 471, 4000, San Miguel de Tucumán, Argentina
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, Universidad Nacional de La Plata (UNLP), Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina
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24
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Kovalishyn V, Severin O, Kachaeva M, Kobzar O, Keith KA, Harden EA, Hartline CB, James SH, Vovk A, Brovarets V. In Silico Design and Experimental Validation of Novel Oxazole Derivatives Against Varicella zoster virus. Mol Biotechnol 2024; 66:707-717. [PMID: 36709460 DOI: 10.1007/s12033-023-00670-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/14/2023] [Indexed: 01/30/2023]
Abstract
Varicella zoster virus (VZV) infection causes severe disease such as chickenpox, shingles, and postherpetic neuralgia, often leading to disability. Reactivation of latent VZV is associated with a decrease in specific cellular immunity in the elderly and in patients with immunodeficiency. However, due to the limited efficacy of existing therapy and the emergence of antiviral resistance, it has become necessary to develop new and effective antiviral drugs for the treatment of diseases caused by VZV, particularly in the setting of opportunistic infections. The goal of this work is to identify potent oxazole derivatives as anti-VZV agents by machine learning, followed by their synthesis and experimental validation. Predictive QSAR models were developed using the Online Chemical Modeling Environment (OCHEM). Data on compounds exhibiting antiviral activity were collected from the ChEMBL and uploaded in the OCHEM database. The predictive ability of the models was tested by cross-validation, giving coefficient of determination q2 = 0.87-0.9. The validation of the models using an external test set proves that the models can be used to predict the antiviral activity of newly designed and known compounds with reasonable accuracy within the applicability domain (q2 = 0.83-0.84). The models were applied to screen a virtual chemical library with expected activity of compounds against VZV. The 7 most promising oxazole derivatives were identified, synthesized, and tested. Two of them showed activity against the VZV Ellen strain upon primary in vitro antiviral screening. The synthesized compounds may represent an interesting starting point for further development of the oxazole derivatives against VZV. The developed models are available online at OCHEM http://ochem.eu/article/145978 and can be used to virtually screen for potential compounds with anti-VZV activity.
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Affiliation(s)
- Vasyl Kovalishyn
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Science of Ukraine, Kyiv, 02094, Ukraine.
| | - Oleksandr Severin
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Science of Ukraine, Kyiv, 02094, Ukraine
| | - Maryna Kachaeva
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Science of Ukraine, Kyiv, 02094, Ukraine
| | - Oleksandr Kobzar
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Science of Ukraine, Kyiv, 02094, Ukraine
| | - Kathy A Keith
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Emma A Harden
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Caroll B Hartline
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Scott H James
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama, 35233, USA
| | - Andriy Vovk
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Science of Ukraine, Kyiv, 02094, Ukraine
| | - Volodymyr Brovarets
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Science of Ukraine, Kyiv, 02094, Ukraine
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25
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Llompart P, Minoletti C, Baybekov S, Horvath D, Marcou G, Varnek A. Will we ever be able to accurately predict solubility? Sci Data 2024; 11:303. [PMID: 38499581 PMCID: PMC10948805 DOI: 10.1038/s41597-024-03105-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/29/2024] [Indexed: 03/20/2024] Open
Abstract
Accurate prediction of thermodynamic solubility by machine learning remains a challenge. Recent models often display good performances, but their reliability may be deceiving when used prospectively. This study investigates the origins of these discrepancies, following three directions: a historical perspective, an analysis of the aqueous solubility dataverse and data quality. We investigated over 20 years of published solubility datasets and models, highlighting overlooked datasets and the overlaps between popular sets. We benchmarked recently published models on a novel curated solubility dataset and report poor performances. We also propose a workflow to cure aqueous solubility data aiming at producing useful models for bench chemist. Our results demonstrate that some state-of-the-art models are not ready for public usage because they lack a well-defined applicability domain and overlook historical data sources. We report the impact of factors influencing the utility of the models: interlaboratory standard deviation, ionic state of the solute and data sources. The herein obtained models, and quality-assessed datasets are publicly available.
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Affiliation(s)
- P Llompart
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
- IDD/CADD, Sanofi, Vitry-Sur-Seine, France
| | | | - S Baybekov
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
| | - D Horvath
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
| | - G Marcou
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France.
| | - A Varnek
- Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France
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26
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Tagorti G, Yalçın B, Güneş M, Burgazlı AY, Kuruca T, Cihanoğlu N, Akarsu E, Kaya N, Marcos R, Kaya B. Alcohol-free synthesis, biological assessment, in vivo toxicological evaluation, and in silico analysis of novel silane quaternary ammonium compounds differing in structure and chain length as promising disinfectants. JOURNAL OF HAZARDOUS MATERIALS 2024; 466:133470. [PMID: 38246053 DOI: 10.1016/j.jhazmat.2024.133470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/28/2023] [Accepted: 01/06/2024] [Indexed: 01/23/2024]
Abstract
Quaternary ammonium compounds (QACs) are commonly used as disinfectants for industrial, medical, and residential applications. However, adverse health outcomes have been reported. Therefore, biocompatible disinfectants must be developed to reduce these adverse effects. In this context, QACs with various alkyl chain lengths (C12-C18) were synthesized by reacting QACs with the counterion silane. The antimicrobial activities of the novel compounds against four strains of microorganisms were assessed. Several in vivo assays were conducted on Drosophila melanogaster to determine the toxicological outcomes of Si-QACs, followed by computational analyses (molecular docking, simulation, and prediction of skin sensitization). The in vivo results were combined using a cheminformatics approach to understand the descriptors responsible for the safety of Si-QAC. Si-QAC-2 was active against all tested bacteria, with minimal inhibitory concentrations ranging from 13.65 to 436.74 ppm. Drosophila exposed to Si-QAC-2 have moderate-to-low toxicological outcomes. The molecular weight, hydrophobicity/lipophilicity, and electron diffraction properties were identified as crucial descriptors for ensuring the safety of the Si-QACs. Furthermore, Si-QAC-2 exhibited good stability and notable antiviral potential with no signs of skin sensitization. Overall, Si-QAC-2 (C14) has the potential to be a novel disinfectant.
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Affiliation(s)
- Ghada Tagorti
- Department of Biology, Akdeniz University, Antalya, Turkey
| | - Burçin Yalçın
- Department of Biology, Akdeniz University, Antalya, Turkey
| | - Merve Güneş
- Department of Biology, Akdeniz University, Antalya, Turkey
| | | | - Tuğçe Kuruca
- Department of Chemistry, Akdeniz University, Antalya, Turkey
| | | | - Esin Akarsu
- Department of Chemistry, Akdeniz University, Antalya, Turkey
| | - Nuray Kaya
- Department of Biology, Akdeniz University, Antalya, Turkey
| | - Ricard Marcos
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
| | - Bülent Kaya
- Department of Biology, Akdeniz University, Antalya, Turkey.
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27
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Erickson M, Casañola-Martin G, Han Y, Rasulev B, Kilin D. Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems. J Phys Chem B 2024; 128:2190-2200. [PMID: 38386478 DOI: 10.1021/acs.jpcb.3c06854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.
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Affiliation(s)
- Meade Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Gerardo Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Yulun Han
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States
| | - Dmitri Kilin
- Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States
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28
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Hunklinger A, Hartog P, Šícho M, Godin G, Tetko IV. The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2024; 29:100144. [PMID: 38316342 DOI: 10.1016/j.slasd.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 01/06/2024] [Accepted: 01/22/2024] [Indexed: 02/07/2024]
Abstract
The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to predict the aqueous solubility of small molecules using experimental data from 100 K compounds. In total, hundred teams took part in the challenge to predict low, medium and highly soluble compounds as measured by the nephelometry assay. This article describes the winning model, which was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM) available on the website https://ochem.eu/article/27. We describe in detail the assumptions and steps used to select methods, descriptors and strategy which contributed to the winning solution. In particular we show that consensus based on 28 models calculated using descriptor-based and representation learning methods allowed us to obtain the best score, which was higher than those based on individual approaches or consensus models developed using each individual approach. A combination of diverse models allowed us to decrease both bias and variance of individual models and to calculate the highest score. The model based on Transformer CNN contributed the best individual score thus highlighting the power of Natural Language Processing (NLP) methods. The inclusion of information about aleatoric uncertainty would be important to better understand and use the challenge data by the contestants.
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Affiliation(s)
- Andrea Hunklinger
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany
| | - Peter Hartog
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany
| | - Martin Šícho
- Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC Leiden, the Netherlands; CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - Guillaume Godin
- dsm-firmenich SA, Rue de la Bergère 7, CH-1242 Satigny, Switzerland
| | - Igor V Tetko
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany; BIGCHEM GmbH, Valerystr. 49, DE-85716 Unterschleißheim, Germany.
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29
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Mondal I, Halder AK, Pattanayak N, Mandal SK, Cordeiro MNDS. Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction. Pharmaceuticals (Basel) 2024; 17:263. [PMID: 38399478 PMCID: PMC10891520 DOI: 10.3390/ph17020263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024] Open
Abstract
Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.
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Affiliation(s)
- Ismail Mondal
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713206, India; (I.M.); (A.K.H.); (N.P.); (S.K.M.)
| | - Amit Kumar Halder
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713206, India; (I.M.); (A.K.H.); (N.P.); (S.K.M.)
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Nirupam Pattanayak
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713206, India; (I.M.); (A.K.H.); (N.P.); (S.K.M.)
| | - Sudip Kumar Mandal
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713206, India; (I.M.); (A.K.H.); (N.P.); (S.K.M.)
| | - Maria Natalia D. S. Cordeiro
- LAQV@REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Yang Z, Wang L, Yang Y, Pang X, Sun Y, Liang Y, Cao H. Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:2817-2829. [PMID: 38291630 DOI: 10.1021/acs.est.3c09779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrine-disrupting effects. In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism. Overall, our computational study is helpful for toxicological screening of BPA alternatives and the design of environmentally friendly BPA alternatives.
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Affiliation(s)
- Zeguo Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ling Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ying Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Xudi Pang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yuzhen Sun
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Huiming Cao
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
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Torigoe T, Takahashi M, Heravizadeh O, Ikeda K, Nakatani K, Bamba T, Izumi Y. Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome. Anal Chem 2024; 96:1275-1283. [PMID: 38186224 DOI: 10.1021/acs.analchem.3c04618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.
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Affiliation(s)
- Taihei Torigoe
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Masatomo Takahashi
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Omidreza Heravizadeh
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Kazuki Ikeda
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Kohta Nakatani
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Takeshi Bamba
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Yoshihiro Izumi
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
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Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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Gryniukova A, Borysko P, Myziuk I, Alieksieieva D, Hodyna D, Semenyuta I, Kovalishyn V, Metelytsia L, Rogalsky S, Tcherniuk S. Anticancer activity features of imidazole-based ionic liquids and lysosomotropic detergents: in silico and in vitro studies. Mol Divers 2024:10.1007/s11030-023-10779-4. [PMID: 38246950 DOI: 10.1007/s11030-023-10779-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 11/20/2023] [Indexed: 01/23/2024]
Abstract
Long-chain imidazole-based ionic liquids (compounds 2, 4, 9) and lysosomotropic detergents (compounds 7, 3, 8) with potent anticancer activity were synthesized. Their inhibitory activities against neuroblastoma and leukaemia cell lines were predicted by the new in silico QSAR models. The cytotoxic activities of the synthesized imidazole derivatives were investigated on the SK-N-DZ (human neuroblastoma) and K-562 (human chronic myeloid leukaemia) cell lines. Compounds 2 and 7 showed the highest in vitro cytotoxic effect on both cancer cell lines. The docking procedure of compounds 2 and 7 into the NAD+ coenzyme binding site of deacetylase Sirtuin-1 (SIRT-1) showed the formation of protein-ligand complexes with calculated binding energies of - 8.0 and - 8.1 kcal/mol, respectively. The interaction of SIRT1 with compounds 2, 7 and 9 and the interaction of Bromodomain-containing protein 4 (BRD4) with compounds 7 and 9 were also demonstrated by thermal shift assay. Compounds 2, 4, 7 and 9 inhibited SIRT1 deacetylase activity in the SIRT-Glo assay. Compounds 7 and 9 showed a moderate inhibitory activity against Aurora kinase A. In addition, compounds 3, 4, 8 and 9 inhibited the Janus kinase 2 activity. The results obtained showed that long-chain imidazole derivatives exhibited cytotoxic activities on K562 leukaemia and SK-N-DZ neuroblastoma cell lines. Furthermore, these compounds inhibited a panel of molecular targets involved in leukaemia and neuroblastoma tumorigenesis. All these results suggest that both long-chain imidazole-based ionic liquids and lysosomotropic detergents may be an effective alternative for the treatment of neuroblastoma and chronic myeloid leukemia and merit further investigation.
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Affiliation(s)
- Anastasiia Gryniukova
- Department of Medical and Biological Researches, V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Academician Kukhar Str, Kyiv, 02094, Ukraine
- Bienta/Enamine Ltd, 78 Winston Churchill Str, Kyiv, 02094, Ukraine
| | - Petro Borysko
- Bienta/Enamine Ltd, 78 Winston Churchill Str, Kyiv, 02094, Ukraine
| | - Iryna Myziuk
- Bienta/Enamine Ltd, 78 Winston Churchill Str, Kyiv, 02094, Ukraine
| | | | - Diana Hodyna
- Department of Medical and Biological Researches, V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Academician Kukhar Str, Kyiv, 02094, Ukraine
| | - Ivan Semenyuta
- Department of Medical and Biological Researches, V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Academician Kukhar Str, Kyiv, 02094, Ukraine
| | - Vasyl Kovalishyn
- Department of Medical and Biological Researches, V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Academician Kukhar Str, Kyiv, 02094, Ukraine
| | - Larysa Metelytsia
- Department of Medical and Biological Researches, V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 1 Academician Kukhar Str, Kyiv, 02094, Ukraine
| | - Sergiy Rogalsky
- Laboratory of Modification of Polymers, V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science of Ukraine, 50 Kharkivske shose, Kyiv, 02160, Ukraine.
| | - Sergey Tcherniuk
- IdeSip, 4 Rue Pierre Fontaine, 91058, Évry-Courcouronnes, France.
- Department of Biological Sciences, Youth Academy of Sciences, 2 Nemyrovych-Danchenko Str, Kyiv, 01011, Ukraine.
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Makhaeva GF, Kovaleva NV, Rudakova EV, Boltneva NP, Lushchekina SV, Astakhova TY, Timokhina EN, Serkov IV, Proshin AN, Soldatova YV, Poletaeva DA, Faingold II, Mumyatova VA, Terentiev AA, Radchenko EV, Palyulin VA, Bachurin SO, Richardson RJ. Combining Experimental and Computational Methods to Produce Conjugates of Anticholinesterase and Antioxidant Pharmacophores with Linker Chemistries Affecting Biological Activities Related to Treatment of Alzheimer's Disease. Molecules 2024; 29:321. [PMID: 38257233 PMCID: PMC10820264 DOI: 10.3390/molecules29020321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
Effective therapeutics for Alzheimer's disease (AD) are in great demand worldwide. In our previous work, we responded to this need by synthesizing novel drug candidates consisting of 4-amino-2,3-polymethylenequinolines conjugated with butylated hydroxytoluene via fixed-length alkylimine or alkylamine linkers (spacers) and studying their bioactivities pertaining to AD treatment. Here, we report significant extensions of these studies, including the use of variable-length spacers and more detailed biological characterizations. Conjugates were potent inhibitors of acetylcholinesterase (AChE, the most active was 17d IC50 15.1 ± 0.2 nM) and butyrylcholinesterase (BChE, the most active was 18d: IC50 5.96 ± 0.58 nM), with weak inhibition of off-target carboxylesterase. Conjugates with alkylamine spacers were more effective cholinesterase inhibitors than alkylimine analogs. Optimal inhibition for AChE was exhibited by cyclohexaquinoline and for BChE by cycloheptaquinoline. Increasing spacer length elevated the potency against both cholinesterases. Structure-activity relationships agreed with docking results. Mixed-type reversible AChE inhibition, dual docking to catalytic and peripheral anionic sites, and propidium iodide displacement suggested the potential of hybrids to block AChE-induced β-amyloid (Aβ) aggregation. Hybrids also exhibited the inhibition of Aβ self-aggregation in the thioflavin test; those with a hexaquinoline ring and C8 spacer were the most active. Conjugates demonstrated high antioxidant activity in ABTS and FRAP assays as well as the inhibition of luminol chemiluminescence and lipid peroxidation in mouse brain homogenates. Quantum-chemical calculations explained antioxidant results. Computed ADMET profiles indicated favorable blood-brain barrier permeability, suggesting the CNS activity potential. Thus, the conjugates could be considered promising multifunctional agents for the potential treatment of AD.
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Affiliation(s)
- Galina F. Makhaeva
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Nadezhda V. Kovaleva
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Elena V. Rudakova
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Natalia P. Boltneva
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Sofya V. Lushchekina
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
- Emanuel Institute of Biochemical Physics Russian Academy of Sciences, Moscow 119334, Russia
| | - Tatiana Y. Astakhova
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
- Emanuel Institute of Biochemical Physics Russian Academy of Sciences, Moscow 119334, Russia
| | - Elena N. Timokhina
- Emanuel Institute of Biochemical Physics Russian Academy of Sciences, Moscow 119334, Russia
| | - Igor V. Serkov
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Alexey N. Proshin
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Yuliya V. Soldatova
- Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (Y.V.S.); (D.A.P.); (I.I.F.); (V.A.M.); (A.A.T.)
| | - Darya A. Poletaeva
- Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (Y.V.S.); (D.A.P.); (I.I.F.); (V.A.M.); (A.A.T.)
| | - Irina I. Faingold
- Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (Y.V.S.); (D.A.P.); (I.I.F.); (V.A.M.); (A.A.T.)
| | - Viktoriya A. Mumyatova
- Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (Y.V.S.); (D.A.P.); (I.I.F.); (V.A.M.); (A.A.T.)
| | - Alexey A. Terentiev
- Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (Y.V.S.); (D.A.P.); (I.I.F.); (V.A.M.); (A.A.T.)
| | - Eugene V. Radchenko
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Vladimir A. Palyulin
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Sergey O. Bachurin
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Chernogolovka 142432, Russia; (G.F.M.); (N.V.K.); (E.V.R.); (N.P.B.); (S.V.L.); (I.V.S.); (A.N.P.); (E.V.R.); (V.A.P.); (S.O.B.)
| | - Rudy J. Richardson
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Center of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Computational Discovery and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Nakatani K, Izumi Y, Umakoshi H, Yokomoto-Umakoshi M, Nakaji T, Kaneko H, Nakao H, Ogawa Y, Ikeda K, Bamba T. Wide-scope targeted analysis of bioactive lipids in human plasma by LC/MS/MS. J Lipid Res 2024; 65:100492. [PMID: 38135255 PMCID: PMC10821590 DOI: 10.1016/j.jlr.2023.100492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/14/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Quantitative information on blood metabolites can be used in developing advanced medical strategies such as early detection and prevention of disease. Monitoring bioactive lipids such as steroids, bile acids, and PUFA metabolites could be a valuable indicator of health status. However, a method for simultaneously measuring these bioactive lipids has not yet been developed. Here, we report a LC/MS/MS method that can simultaneously measure 144 bioactive lipids, including steroids, bile acids, and PUFA metabolites, from human plasma, and a sample preparation method for these targets. Protein removal by methanol precipitation and purification of bioactive lipids by solid-phase extraction improved the recovery of the targeted compounds in human plasma samples, demonstrating the importance of sample preparation methods for a wide range of bioactive lipid analyses. Using the developed method, we studied the plasma from healthy human volunteers and confirmed the presence of bioactive lipid molecules associated with sex differences and circadian rhythms. The developed method of bioactive lipid analysis can be applied to health monitoring and disease biomarker discovery in precision medicine.
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Affiliation(s)
- Kohta Nakatani
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Yoshihiro Izumi
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
| | - Hironobu Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Maki Yokomoto-Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomoko Nakaji
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hiroki Kaneko
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hiroshi Nakao
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kazutaka Ikeda
- Laboratory of Biomolecule Analysis, Department of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan
| | - Takeshi Bamba
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
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Mitra S, Chatterjee S, Bose S, Panda P, Basak S, Ghosh N, Mandal SC, Singhmura S, Halder AK. Finding structural requirements of structurally diverse α-glucosidase and α-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses. J Mol Graph Model 2024; 126:108640. [PMID: 37801809 DOI: 10.1016/j.jmgm.2023.108640] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/09/2023] [Accepted: 09/26/2023] [Indexed: 10/08/2023]
Abstract
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemic state. The α-glucosidase and α-amylase are considered two major targets for the management of Type 2 DM due to their ability of metabolizing carbohydrates into simpler sugars. In the current study, cheminformatics analyses were performed to develop validated and predictive models with a dataset of 187 α-glucosidase and α-amylase dual inhibitors. Separate linear, interpretable and statistically robust 2D-QSAR models were constructed with datasets containing the activities of α-glucosidase and α-amylase inhibitors with an aim to explain the crucial structural and physicochemical attributes responsible for higher activity towards these targets. Consequently, some descriptors of the models pointed out the importance of specific structural moieties responsible for the higher activities for these targets and on the other hand, properties such as ionization potential and mass of the compounds as well as number of hydrogen bond donors in molecules were found to be crucial in determining the binding potentials of the dataset compounds. Statistically significant 3D-QSAR models were developed with both α-glucosidase and α-amylase inhibition datapoints to estimate the importance of 3D electrostatic and steric fields for improved potentials towards these two targets. Molecular docking performed with selected compounds with homology model of α-glucosidase and X-ray crystal structure of α-amylase largely supported the interpretations obtained from the cheminformatic analyses. The current investigation should serve as important guidelines for the design of future α-glucosidase and α-amylase inhibitors. Besides, the current investigation is entirely performed by using non-commercial open-access tools to ensure easy accessibility and reproducibility of the investigation which may help researchers throughout the world to work more on drug design and discovery.
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Affiliation(s)
- Soumya Mitra
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India
| | - Subhadas Chatterjee
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India
| | - Shobhan Bose
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India
| | - Parthasarathi Panda
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India
| | - Souvik Basak
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India
| | - Nilanjan Ghosh
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
| | - Subhash C Mandal
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Saroj Singhmura
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India
| | - Amit Kumar Halder
- Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India.
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Nath A, Chaube R. Mining Chemogenomic Spaces for Prediction of Drug-Target Interactions. Methods Mol Biol 2024; 2714:155-169. [PMID: 37676598 DOI: 10.1007/978-1-0716-3441-7_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug-target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor.
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Affiliation(s)
- Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, India
| | - Radha Chaube
- Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, India
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Aires-de-Sousa J. GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit. Mol Inform 2024; 43:e202300190. [PMID: 37885368 DOI: 10.1002/minf.202300190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 10/28/2023]
Abstract
GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol.
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Affiliation(s)
- Joao Aires-de-Sousa
- LAQV and REQUIMTE, Chemistry Department, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal
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Koscher BA, Canty RB, McDonald MA, Greenman KP, McGill CJ, Bilodeau CL, Jin W, Wu H, Vermeire FH, Jin B, Hart T, Kulesza T, Li SC, Jaakkola TS, Barzilay R, Gómez-Bombarelli R, Green WH, Jensen KF. Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science 2023; 382:eadi1407. [PMID: 38127734 DOI: 10.1126/science.adi1407] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023]
Abstract
A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.
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Affiliation(s)
- Brent A Koscher
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Richard B Canty
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew A McDonald
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin P Greenman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Charles J McGill
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Camille L Bilodeau
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wengong Jin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haoyang Wu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Florence H Vermeire
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brooke Jin
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Travis Hart
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Timothy Kulesza
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shih-Cheng Li
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tommi S Jaakkola
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Klavs F Jensen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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40
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La Paglia L, Vazzana M, Mauro M, Urso A, Arizza V, Vizzini A. Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Mar Drugs 2023; 22:6. [PMID: 38276644 PMCID: PMC10817596 DOI: 10.3390/md22010006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.
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Affiliation(s)
- Laura La Paglia
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Mirella Vazzana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Manuela Mauro
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Alfonso Urso
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Vincenzo Arizza
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Aiti Vizzini
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
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Sandhu H, Garg P. Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism. Chem Res Toxicol 2023; 36:1876-1890. [PMID: 37885227 DOI: 10.1021/acs.chemrestox.3c00162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.
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Affiliation(s)
- Hardeep Sandhu
- Department of pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar 160062, Punjab, India
| | - Prabha Garg
- Department of pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar 160062, Punjab, India
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42
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Gheta SKO, Bonin A, Gerlach T, Göller AH. Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state. J Comput Aided Mol Des 2023; 37:765-789. [PMID: 37878216 DOI: 10.1007/s10822-023-00538-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/02/2023] [Indexed: 10/26/2023]
Abstract
In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.
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Affiliation(s)
- Sadra Kashef Ol Gheta
- Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096, Wuppertal, Germany
| | - Anne Bonin
- Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096, Wuppertal, Germany
| | - Thomas Gerlach
- Bayer AG, Crop Science, R&D, Digital Transformation, 40789, Monheim, Germany
- Bayer AG, Engineering & Technology, Thermal Separation Technologies, 51368, Leverkusen, Germany
| | - Andreas H Göller
- Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096, Wuppertal, Germany.
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43
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Hodyna D, Kovalishyn V, Kachaeva M, Shulha Y, Klipkov A, Shaitanova E, Kobzar O, Shablykin O, Metelytsia L. In Silico, in Vitro and in Vivo Study of Substituted Imidazolidinone Sulfonamides as Antibacterial Agents. Chem Biodivers 2023; 20:e202301267. [PMID: 37943002 DOI: 10.1002/cbdv.202301267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/10/2023]
Abstract
New substituted imidazolidinone sulfonamides have been developed using a rational drug design strategy. Predictive QSAR models for the search of new antibacterials were created using the OCHEM platform. Regression models were applied to verify a virtual chemical library of new imidazolidinone derivatives designed to have antibacterial activity. A number of substituted imidazolidinone sulfonamides as effective antibacterial agents were identified by QSAR prediction, synthesized and characterized by spectral and elemental, and tested in vitro. Six studied compounds have shown the highest in vitro antibacterial activity against Gram-negative E. coli and Gram-positive S. aureus multidrug-resistant strains. The in vivo acute toxicity of these imidazolidinone sulfonamides based on the LC50 value ranged from 16.01 to 44.35 mg/L (slightly toxic compounds class). The results of molecular docking suggest that the antibacterial mechanism of the compounds can be associated with the inhibition of post-translational modification processes of bacterial peptides and proteins.
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Affiliation(s)
- Diana Hodyna
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Vasyl Kovalishyn
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Maryna Kachaeva
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Yurii Shulha
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Anton Klipkov
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Elena Shaitanova
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Oleksandr Kobzar
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Oleh Shablykin
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
| | - Larysa Metelytsia
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine, 02094, Academician Kukhar Str, 1, Kyiv, Ukraine
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Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics 2023; 15:2619. [PMID: 38004597 PMCID: PMC10675155 DOI: 10.3390/pharmaceutics15112619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.
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Affiliation(s)
- Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, Japan
| | - Reiko Watanabe
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| | - Kenji Mizuguchi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
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Vasilenko DA, Temnyakova NS, Dronov SE, Radchenko EV, Grishin YK, Gabrel’yan AV, Zamoyski VL, Grigoriev VV, Averina EB, Palyulin VA. 5-Nitroisoxazoles in SNAr Reactions: A Novel Chemo- and Regioselective Approach to Isoxazole-Based Bivalent Ligands of AMPA Receptors. Int J Mol Sci 2023; 24:16135. [PMID: 38003327 PMCID: PMC10671298 DOI: 10.3390/ijms242216135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 10/29/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
An efficient regioselective approach to novel functionalized bis(isoxazoles) with a variety of aromatic and aliphatic linkers was elaborated, based on the heterocyclization reaction of electrophilic alkenes under the treatment with tetranitromethane-triethylamine complex affording 3-EWG-5-nitroisoxazoles. The subsequent SNAr reactions of 5-nitroisoxazoles with various O,O-, N,N- and S,S-bis(nucleophiles) provide a wide range of bis(isoxazole) derivatives in good isolated yields. Employing an elaborated method, a series of novel bis(3-EWG-isoxazoles) as the promising allosteric modulators of AMPA receptors were designed and synthesized. The effect of the compounds on the kainate-induced currents was studied in the patch clamp experiments, revealing modulator properties for several of them. The best positive modulator potency was found for dimethyl 5,5'-(ethane-1,2-diylbis(sulfanediyl))bis(isoxazole-3-carboxylate), which potentiated the kainate-induced currents in a wide concentration range (10-12-10-6 M) with maximum potentiation of 77% at 10-10 M. The results were rationalized using molecular docking and molecular dynamics simulations of modulator complexes with the dimeric ligand-binding domain of the GluA2 AMPA receptor. The predicted physicochemical, ADMET, and PAINS properties confirmed that the AMPA receptor modulators based on the bis(isoxazole) scaffold may serve as potential lead compounds for the development of neuroprotective drugs.
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Affiliation(s)
- Dmitry A. Vasilenko
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
| | - Nadezhda S. Temnyakova
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
| | - Sevastian E. Dronov
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
| | - Eugene V. Radchenko
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
| | - Yuri K. Grishin
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
| | - Alexey V. Gabrel’yan
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Moscow Region, Russia; (A.V.G.); (V.L.Z.)
| | - Vladimir L. Zamoyski
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Moscow Region, Russia; (A.V.G.); (V.L.Z.)
| | - Vladimir V. Grigoriev
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Severny proezd 1, 142432 Chernogolovka, Moscow Region, Russia; (A.V.G.); (V.L.Z.)
| | - Elena B. Averina
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
| | - Vladimir A. Palyulin
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, 119991 Moscow, Russia; (D.A.V.); (N.S.T.); (S.E.D.); (E.V.R.); (Y.K.G.); (V.V.G.)
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46
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Tran TTV, Tayara H, Chong KT. Recent Studies of Artificial Intelligence on In Silico Drug Absorption. J Chem Inf Model 2023; 63:6198-6211. [PMID: 37819031 DOI: 10.1021/acs.jcim.3c00960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.
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Affiliation(s)
- Thi Tuyet Van Tran
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
- Faculty of Information Technology, An Giang University, Long Xuyen 880000, Vietnam
- Vietnam National University, Ho Chi Minh City, Ho Chi Minh 700000, Vietnam
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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47
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Zhang R, Wang B, Li L, Li S, Guo H, Zhang P, Hua Y, Cui X, Li Y, Mu Y, Huang X, Li X. Modeling and insights into the structural characteristics of endocrine-disrupting chemicals. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115251. [PMID: 37451095 DOI: 10.1016/j.ecoenv.2023.115251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/03/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.
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Affiliation(s)
- Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Bailun Wang
- Department of Anesthesiology and perioperative medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Anesthesia and Respiratory Intensive Care Medicine, Jinan 250014, China
| | - Ling Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Shengjie Li
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan 250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Mu
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xin Huang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
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48
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Zhang R, Chen Z, Wang B, Li Y, Mu Y, Li X. Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity. ACS OMEGA 2023; 8:31675-31682. [PMID: 37692239 PMCID: PMC10483523 DOI: 10.1021/acsomega.3c01725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023]
Abstract
Mitochondria are the energy metabolism center of cells and are involved in a number of other processes, such as cell differentiation and apoptosis, signal transduction, and regulation of cell cycle and cell proliferation. It is of great significance to evaluate the mitochondrial toxicity of drugs and other chemicals. In the present study, we aimed to propose easily available artificial intelligence (AI) models for the prediction of chemical mitochondrial toxicity and investigate the structural characteristics with the analysis of molecular properties and structural alerts. The consensus model achieved good predictive results with high total accuracy at 87.21% for validation sets. The models can be accessed freely via https://ochem.eu/article/158582. Besides, several commonly used chemical properties were significantly different between chemicals with and without mitochondrial toxicity. We also detected the structural alerts (SAs) responsible for mitochondrial toxicity and integrated them into the web-server SApredictor (www.sapredictor.cn). The study may provide useful tools for in silico estimation of mitochondrial toxicity and be helpful to understand the mechanisms of mitochondrial toxicity.
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Affiliation(s)
- Ruiqiu Zhang
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Zhaoyang Chen
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Baobao Wang
- Department
of Nephrology, The First Affiliated Hospital
of Shandong First Medical University & Shandong Provincial Qianfoshan
Hospital, Jinan 250014, China
| | - Yan Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Mu
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
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49
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Wang Y, Xiong J, Xiao F, Zhang W, Cheng K, Rao J, Niu B, Tong X, Qu N, Zhang R, Wang D, Chen K, Li X, Zheng M. LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP. J Cheminform 2023; 15:76. [PMID: 37670374 PMCID: PMC10478446 DOI: 10.1186/s13321-023-00754-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023] Open
Abstract
Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.
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Affiliation(s)
- Yitian Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jiacheng Xiong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Fu Xiao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, China
| | - Wei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Kaiyang Cheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Buying Niu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Runze Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | | | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, China.
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50
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Hodyna D, Kovalishyn V, Romanenko Y, Semenyuta I, Blagodatny V, Kachaeva M, Brazhko O, Metelytsia L. Quinoline Hydrazone Derivatives as New Antibacterials against Multidrug Resistant Strains. Chem Biodivers 2023; 20:e202300839. [PMID: 37552570 DOI: 10.1002/cbdv.202300839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 08/10/2023]
Abstract
To develop novel antimicrobial agents a series of 2(4)-hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti-E. coli activity and screened using the regression model of anti-S. aureus activity. Selected and synthesized 2(4)-hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic-resistant S. aureus and E. coli strains in the range from 15 to 30 mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from -8.4 to -9.4 kcal/mol.
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Affiliation(s)
- Diana Hodyna
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of, National Academy of Science of Ukraine, Kyiv, 02094, Academician Kukhar Str., 1, Ukraine
| | - Vasyl Kovalishyn
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of, National Academy of Science of Ukraine, Kyiv, 02094, Academician Kukhar Str., 1, Ukraine
| | - Yanina Romanenko
- Zaporizhzhya National University, Faculty of Biology, Zaporizhzhya, 69095, Zhukovs'ky Str., 66, Ukraine
| | - Ivan Semenyuta
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of, National Academy of Science of Ukraine, Kyiv, 02094, Academician Kukhar Str., 1, Ukraine
| | - Volodymyr Blagodatny
- Shupyk National Healthcare University of Ukraine, Kyiv, 04112, Dorogozhytska Str., 9, Ukraine
| | - Maryna Kachaeva
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of, National Academy of Science of Ukraine, Kyiv, 02094, Academician Kukhar Str., 1, Ukraine
| | - Oleksandr Brazhko
- Zaporizhzhya National University, Faculty of Biology, Zaporizhzhya, 69095, Zhukovs'ky Str., 66, Ukraine
| | - Larysa Metelytsia
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of, National Academy of Science of Ukraine, Kyiv, 02094, Academician Kukhar Str., 1, Ukraine
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