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Manaithiya A, Bhowmik R, Acharjee S, Sharma S, Kumar S, Imran M, Mathew B, Parkkila S, Aspatwar A. Elucidating molecular mechanism and chemical space of chalcones through biological networks and machine learning approaches. Comput Struct Biotechnol J 2024; 23:2811-2836. [PMID: 39045026 PMCID: PMC11263914 DOI: 10.1016/j.csbj.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/25/2024] Open
Abstract
We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models (https://github.com/RatulChemoinformatics/QSAR) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90AA1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90AA1, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90AA1 and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.
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Affiliation(s)
- Ajay Manaithiya
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Ratul Bhowmik
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Satarupa Acharjee
- Department of Pharmacy, NSHM Knowledge Campus, Kolkata-Group of Institutions, Kolkata, West Bengal 700053, India
| | - Sameer Sharma
- Department of Bioinformatics, BioNome, Bangalore 560043, India
| | - Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS, Health Sciences Campus, Kochi, India
| | - Mohd. Imran
- Department of Pharmaceutical Chemistry, College of Pharmacy, Northern Border University, Rafha 91911, Saudi Arabia
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS, Health Sciences Campus, Kochi, India
| | - Seppo Parkkila
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Ltd., Tampere University Hospital, Tampere, Finland
| | - Ashok Aspatwar
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Singh R, Sindhu J, Devi M, Kumar P, Lal S, Kumar A, Singh D, Kumar H. Synthesis of thiazolidine-2,4-dione tethered 1,2,3-triazoles as α-amylase inhibitors: In vitro approach coupled with QSAR, molecular docking, molecular dynamics and ADMET studies. Eur J Med Chem 2024; 275:116623. [PMID: 38943875 DOI: 10.1016/j.ejmech.2024.116623] [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: 03/17/2024] [Revised: 05/08/2024] [Accepted: 06/22/2024] [Indexed: 07/01/2024]
Abstract
A new series of thiazolidine-2,4-dione tethered 1,2,3-triazole derivatives were designed, synthesized and screened for their α-amylase inhibitory potential employing in vitro and in silico approaches. The target compounds were synthesized with the help of Cu (I) catalyzed [3 + 2] cycloaddition of terminal alkyne with numerous azides, followed by unambiguously characterizing the structure by employing various spectroscopic approaches. The synthesized derivatives were assessed for their in vitro α-amylase inhibition and it was found that thiazolidine-2,4-dione derivatives 6e, 6j, 6o, 6u and 6x exhibited comparable inhibition with the standard drug acarbose. The compound 6e with a 7-chloroquinolinyl substituent on the triazole ring exhibited significant inhibition potential with IC50 value of 0.040 μmol mL-1 whereas compound 6c (IC50 = 0.099 μmol mL-1) and 6h (IC50 = 0.098 μmol mL-1) were poor inhibitors. QSAR studies revealed the positively correlating descriptors that aid in the design of novel compounds. Molecular docking was performed to investigate the binding interactions with the active site of the biological receptor and the stability of the complex over a period of 100 ns was examined using molecular dynamics studies. The physiochemical properties and drug-likeliness behavior of the potent derivatives were investigated by carrying out the ADMET studies.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India; School of Chemistry, Indian Institutes of Science Education and Research, Thiruvananthapuram, Kerala, 695551, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India.
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, -125001, India
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India, 124001
| | - Harish Kumar
- Department of Chemistry, School of Basic Sciences, Central University Haryana, Mahendergarh, India
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Kumar A, Ojha PK, Roy K. Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2024; 273:106985. [PMID: 38875952 DOI: 10.1016/j.aquatox.2024.106985] [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: 04/26/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 06/16/2024]
Abstract
In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC50, prolonged_14D_NOEC, prolonged_21D_LC50, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R2 =0.738-0.869, QLOO2 =0.712-0.831, Q(F1)2 =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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Saliu JA. Machine Learning-Based Approach to Identify Inhibitors of Sterol-14-Alpha Demethylase: A Study on Chagas Disease. Bioinform Biol Insights 2024; 18:11779322241262635. [PMID: 39081668 PMCID: PMC11287730 DOI: 10.1177/11779322241262635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 05/23/2024] [Indexed: 08/02/2024] Open
Abstract
Objectives Chagas Disease, caused by the parasite Trypanosoma cruzi, remains a significant public health concern, particularly in Latin America. The current standard treatment for Chagas Disease, benznidazole, is associated with various side effects, necessitating the search for alternative therapeutic options. In this study, we aimed to identify potential therapeutics for Chagas Disease through a comprehensive computational analysis. Methods A library of compounds derived from Cananga odorata was screened using a combination of pharmacophore modeling, structure-based screening, and quantitative structure-activity relationship (QSAR) analysis. The pharmacophore model facilitated the efficient screening of the compound library, while the structure-based screening identified hit compounds with promising inhibitory potential against the target enzyme, sterol-14-alpha demethylase. Results The QSAR model predicted the bioactivity of the hit compounds, revealing one compound to exhibit superior activity compared to benznidazole. Evaluation of the physicochemical, pharmacokinetic, toxicity, and medicinal chemistry properties of the hit compounds indicated their drug-like characteristics, oral bioavailability, ease of synthesis, and reduced toxicity profiles. Conclusion Overall, our findings present a promising avenue for the discovery of novel therapeutics for Chagas Disease. The identified hit compounds possess favorable drug-like properties and demonstrate potent inhibitory effects against the target enzyme. Further in vitro and in vivo studies are warranted to validate their efficacy and safety profiles.
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Affiliation(s)
- Jamiyu A Saliu
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Nigeria
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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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Ja’afaru SC, Uzairu A, Bayil I, Sallau MS, Ndukwe GI, Ibrahim MT, Moin AT, Mollah AKMM, Absar N. Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions. PLoS One 2024; 19:e0302390. [PMID: 38923997 PMCID: PMC11207139 DOI: 10.1371/journal.pone.0302390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 06/28/2024] Open
Abstract
Schistosomiasis is a neglected tropical disease which imposes a considerable and enduring impact on affected regions, leading to persistent morbidity, hindering child development, diminishing productivity, and imposing economic burdens. Due to the emergence of drug resistance and limited management options, there is need to develop additional effective inhibitors for schistosomiasis. In view of this, quantitative structure-activity relationship studies, molecular docking, molecular dynamics simulations, drug-likeness and pharmacokinetics predictions were applied to 39 Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR) inhibitors. The chosen QSAR model demonstrated robust statistical parameters, including an R2 of 0.798, R2adj of 0.767, Q2cv of 0.681, LOF of 0.930, R2test of 0.776, and cR2p of 0.746, confirming its reliability. The most active derivative (compound 40) was identified as a lead candidate for the development of new potential non-covalent inhibitors through ligand-based design. Subsequently, 12 novel compounds (40a-40l) were designed with enhanced anti-schistosomiasis activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules. Furthermore, drug-likeness and pharmacokinetics prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for the treatment of schistosomiasis.
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Affiliation(s)
- Saudatu Chinade Ja’afaru
- Department of Chemistry Ahmadu Bello University Zaria, Zaria, Nigeria
- Department of Chemistry, Aliko Dangote University of Science and Technology, Wudil, Kano, Nigeria
| | - Adamu Uzairu
- Department of Chemistry Ahmadu Bello University Zaria, Zaria, Nigeria
| | - Imren Bayil
- Department of Bioinformatics and Computational Biology, Gaziantep University, Gaziantep, Turkey
| | | | | | | | - Abu Tayab Moin
- Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chattogram, Bangladesh
| | | | - Nurul Absar
- Department of Biochemistry and Biotechnology, Faculty of Basic Medical and Pharmaceutical Sciences, University of Science & Technology Chittagong, Khulshi, Chittagong, Bangladesh
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Khatun S, Dasgupta I, Islam R, Amin SA, Jha T, Dhaked DK, Gayen S. Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling. Mol Divers 2024:10.1007/s11030-024-10903-y. [PMID: 38871969 DOI: 10.1007/s11030-024-10903-y] [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: 03/17/2024] [Accepted: 05/20/2024] [Indexed: 06/15/2024]
Abstract
Histone deacetylases constitute a group of enzymes that participate in several biological processes. Notably, inhibiting HDAC8 has become a therapeutic strategy for various diseases. The current inhibitors for HDAC8 lack selectivity and target multiple HDACs. Consequently, there is a growing recognition of the need for selective HDAC8 inhibitors to enhance the effectiveness of therapeutic interventions. In our current study, we have utilized a multi-faceted approach, including Quantitative Structure-Activity Relationship (QSAR) combined with Quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling, pharmacophore mapping, molecular docking, and molecular dynamics (MD) simulations. The developed q-RASAR model has a high statistical significance and predictive ability (Q2F1:0.778, Q2F2:0.775). The contributions of important descriptors are discussed in detail to gain insight into the crucial structural features in HDAC8 inhibition. The best pharmacophore hypothesis exhibits a high regression coefficient (0.969) and a low root mean square deviation (0.944), highlighting the importance of correctly orienting hydrogen bond acceptor (HBA), ring aromatic (RA), and zinc-binding group (ZBG) features in designing potent HDAC8 inhibitors. To confirm the results of q-RASAR and pharmacophore mapping, molecular docking analysis of the five potent compounds (44, 54, 82, 102, and 118) was performed to gain further insights into these structural features crucial for interaction with the HDAC8 enzyme. Lastly, MD simulation studies of the most active compound (54, mapped correctly with the pharmacophore hypothesis) and the least active compound (34, mapped poorly with the pharmacophore hypothesis) were carried out to validate the observations of the studies above. This study not only refines our understanding of essential structural features for HDAC8 inhibition but also provides a robust framework for the rational design of novel selective HDAC8 inhibitors which may offer insights to medicinal chemists and researchers engaged in the development of HDAC8-targeted therapeutics.
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Affiliation(s)
- Samima Khatun
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Indrasis Dasgupta
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Rakibul Islam
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Kolkata, West Bengal, 700054, India
| | - Sk Abdul Amin
- Department of Pharmaceutical Technology, JIS University, 81, Nilgunj Road, Agarpara, Kolkata, West Bengal, India
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Devendra Kumar Dhaked
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Kolkata, West Bengal, 700054, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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Paege N, Feustel S, Marx-Stoelting P. Toxicological evaluation of microbial secondary metabolites in the context of European active substance approval for plant protection products. Environ Health 2024; 23:52. [PMID: 38835048 DOI: 10.1186/s12940-024-01092-0] [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: 01/16/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024]
Abstract
Risk assessment (RA) of microbial secondary metabolites (SM) is part of the EU approval process for microbial active substances (AS) used in plant protection products (PPP). As the number of potentially produced microbial SM may be high for a certain microbial strain and existing information on the metabolites often are low, data gaps are frequently identified during the RA. Often, RA cannot conclusively clarify the toxicological relevance of the individual substances. This work presents data and RA conclusions on four metabolites, Beauvericin, 2,3-deepoxy-2,3-didehydro-rhizoxin (DDR), Leucinostatin A and Swainsonin in detail as examples for the challenging process of RA. To overcome the problem of incomplete assessment reports, RA of microbial AS for PPP is in need of new approaches. In view of the Next Generation Risk Assessment (NGRA), the combination of literature data, omic-methods, in vitro and in silico methods combined in adverse outcome pathways (AOPs) can be used for an efficient and targeted identification and assessment of metabolites of concern (MoC).
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Affiliation(s)
- Norman Paege
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
| | - Sabrina Feustel
- German Federal Institute for Risk Assessment (BfR), Berlin, Germany
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Kumar A, Ojha PK, Roy K. The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2024; 26:870-881. [PMID: 38652036 DOI: 10.1039/d4em00059e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.
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Affiliation(s)
- Ankur Kumar
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Probir Kumar Ojha
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
| | - Kunal Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
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10
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Rodríguez-Belenguer P, Mangas-Sanjuan V, Soria-Olivas E, Pastor M. Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis. J Chem Inf Model 2024; 64:2775-2788. [PMID: 37660324 PMCID: PMC11005038 DOI: 10.1021/acs.jcim.3c00945] [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: 06/22/2023] [Indexed: 09/05/2023]
Abstract
Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.
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Affiliation(s)
- Pablo Rodríguez-Belenguer
- Research
Programme on Biomedical Informatics (GRIB), Department of Medicine
and Life Sciences, Universitat Pompeu Fabra,
Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
- Department
of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, 46100 Valencia, Spain
| | - Victor Mangas-Sanjuan
- Department
of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, 46100 Valencia, Spain
- Interuniversity
Research Institute for Molecular Recognition and Technological Development, Universitat Politècnica de València, 46100 Valencia, Spain
| | - Emilio Soria-Olivas
- IDAL,
Intelligent Data Analysis Laboratory, ETSE, Universitat de València, 46100 Valencia, Spain
| | - Manuel Pastor
- Research
Programme on Biomedical Informatics (GRIB), Department of Medicine
and Life Sciences, Universitat Pompeu Fabra,
Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
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Kumar S, Bhowmik R, Oh JM, Abdelgawad MA, Ghoneim MM, Al-Serwi RH, Kim H, Mathew B. Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors. Sci Rep 2024; 14:4868. [PMID: 38418571 PMCID: PMC10901862 DOI: 10.1038/s41598-024-55628-y] [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: 01/04/2024] [Accepted: 02/26/2024] [Indexed: 03/01/2024] Open
Abstract
Monoamine oxidases (MAOs), specifically MAO-A and MAO-B, play important roles in the breakdown of monoamine neurotransmitters. Therefore, MAO inhibitors are crucial for treating various neurodegenerative disorders, including Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, we developed a novel cheminformatics pipeline by generating three diverse molecular feature-based machine learning-assisted quantitative structural activity relationship (ML-QSAR) models concerning MAO-B inhibition. PubChem fingerprints, substructure fingerprints, and one-dimensional (1D) and two-dimensional (2D) molecular descriptors were implemented to unravel the structural insights responsible for decoding the origin of MAO-B inhibition in 249 non-reductant molecules. Based on a random forest ML algorithm, the final PubChem fingerprint, substructure fingerprint, and 1D and 2D molecular descriptor prediction models demonstrated significant robustness, with correlation coefficients of 0.9863, 0.9796, and 0.9852, respectively. The significant features of each predictive model responsible for MAO-B inhibition were extracted using a comprehensive variance importance plot (VIP) and correlation matrix analysis. The final predictive models were further developed as a web application, MAO-B-pred ( https://mao-b-pred.streamlit.app/ ), to allow users to predict the bioactivity of molecules against MAO-B. Molecular docking and dynamics studies were conducted to gain insight into the atomic-level molecular interactions between the ligand-receptor complexes. These findings were compared with the structural features obtained from the ML-QSAR models, which supported the mechanistic understanding of the binding phenomena. The presented models have the potential to serve as tools for identifying crucial molecular characteristics for the rational design of MAO-B target inhibitors, which may be used to develop effective drugs for neurodegenerative disorders.
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Affiliation(s)
- Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Ratul Bhowmik
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Jong Min Oh
- Department of Pharmacy, and Research Institute of Life Pharmaceutical Sciences, Sunchon National University, Suncheon, 57922, Republic of Korea
| | - Mohamed A Abdelgawad
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, 72341, Sakaka, Aljouf, Saudi Arabia
| | - Mohammed M Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, 13713, Ad Diriyah, Riyadh, Saudi Arabia
| | - Rasha Hamed Al-Serwi
- Department of Basic Dental Sciences, College of Dentistry, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Hoon Kim
- Department of Pharmacy, and Research Institute of Life Pharmaceutical Sciences, Sunchon National University, Suncheon, 57922, Republic of Korea.
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India.
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12
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Martinez-Mayorga K, Rosas-Jiménez JG, Gonzalez-Ponce K, López-López E, Neme A, Medina-Franco JL. The pursuit of accurate predictive models of the bioactivity of small molecules. Chem Sci 2024; 15:1938-1952. [PMID: 38332817 PMCID: PMC10848664 DOI: 10.1039/d3sc05534e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.
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Affiliation(s)
- Karina Martinez-Mayorga
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José G Rosas-Jiménez
- Department of Theoretical Biophysics, IMPRS on Cellular Biophysics Max-von-Laue Strasse 3 Frankfurt am Main 60438 Germany
| | - Karla Gonzalez-Ponce
- Institute of Chemistry, Merida Unit, National Autonomous University of Mexico Merida-Tetiz Highway, Km. 4.5 Ucu Yucatan Mexico
| | - Edgar López-López
- Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute Mexico City 07000 Mexico
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
| | - Antonio Neme
- Institute for Applied Mathematics and Systems, Merida Research Unit, National Autonomous University of Mexico Sierra Papacal Merida Yucatan Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry National Autonomous University of Mexico Mexico City 04510 Mexico
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13
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Gallagher A, Kar S. Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR. CHEMOSPHERE 2024; 349:140810. [PMID: 38029938 DOI: 10.1016/j.chemosphere.2023.140810] [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: 10/14/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.
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Affiliation(s)
- Andrea Gallagher
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA.
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14
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Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL, Egieyeh E, Sibuyi NR, Meyer M, Madiehe AM, Wyckoff GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials. Comput Biol Med 2024; 169:107927. [PMID: 38184864 DOI: 10.1016/j.compbiomed.2024.107927] [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: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
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Affiliation(s)
- Samson O Oselusi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Phumuzile Dube
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Kolajo A Akinyede
- Department of Science Technology, Biochemistry Unit, The Federal Polytechnic P.M.B.5351, Ado Ekiti, 360231, Nigeria
| | - Tosin L Ilori
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Elizabeth Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Nicole Rs Sibuyi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Mervin Meyer
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Abram M Madiehe
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Gerald J Wyckoff
- School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri, Kansas City, MO, 64110-2446, United States
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
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15
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Qin R, Zhang H, Huang W, Shao Z, Lei J. Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A 2B receptor antagonists. J Biomol Struct Dyn 2023:1-17. [PMID: 38133953 DOI: 10.1080/07391102.2023.2295974] [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: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
The Adenosine A2B receptor (A2BAR) is considered a novel potential target for the immunotherapy of cancer, and A2BAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of benzimidazole-pyrazine scaffolds whose derivatives exhibited the antagonistic effect but lacked subtype selectivity towards A2BAR. In this work, we developed a scaffold-based protocol that incorporates a deep generative model and multilayer virtual screening to design benzimidazole-pyrazine derivatives as potential selective A2BAR antagonists. By utilizing a generative model with reported A2BAR antagonists as the training set, we built up a scaffold-focused library of benzimidazole-pyrazine derivatives and processed a virtual screening protocol to discover potential A2BAR antagonists. Finally, five molecules with different Bemis-Murcko scaffolds were identified and exhibited higher binding free energies than the reference molecule 12o. Further computational analysis revealed that the 3-benzyl derivative ABA-1266 presented high selectivity toward A2BAR and showed preferred draggability, providing future potent development of selective A2BAR antagonists.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rui Qin
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Hao Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Weifeng Huang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Zhenglin Shao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Jinping Lei
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
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16
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Kolesnyk S, Prodanchuk M, Zhminko P, Kolianchuk Y, Bubalo N, Odermatt A, Smieško M. A battery of in silico models application for pesticides exerting reproductive health effects: Assessment of performance and prioritization of mechanistic studies. Toxicol In Vitro 2023; 93:105706. [PMID: 37802305 DOI: 10.1016/j.tiv.2023.105706] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/12/2023] [Accepted: 10/02/2023] [Indexed: 10/08/2023]
Abstract
Given the high attention to endocrine disrupting chemicals (EDC), there is an urgent need for the development of rapid and reliable approaches for the screening of large numbers of chemicals with respect to their endocrine disruption potential. This study aimed at the assessment of the correlation between the predicted results of a battery of in silico tools and the reported observed adverse effects from in vivo reproductive toxicity studies. We used VirtualToxLab (VTL) software and the EndocrineDisruptome (ED) online tool to evaluate the binding affinities to nuclear receptors of 17 pesticides, 7 of which were classified as reprotoxic substances under Regulation (EC) No 1272/2008 on the classification, labelling and packaging of substances and mixtures (CLP). Then, we aligned the results of the in silico modelling with data from ToxCast assays and in vivo reproductive toxicity studies. We combined results from different in silico tools in two different ways to improve the characteristics of their predictive performance. Reproductive toxicity can be caused by various mechanisms; however, in this study, we demonstrated that the use of a battery of in silico tools for assessing the binding to nuclear receptors can be useful for identifying hazardous compounds and for prioritizing further studies.
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Affiliation(s)
- Serhii Kolesnyk
- L.I. Medved's Research Center of Preventive Toxicology, Food and Chemical Safety, Kyiv, Ukraine; Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Mykola Prodanchuk
- L.I. Medved's Research Center of Preventive Toxicology, Food and Chemical Safety, Kyiv, Ukraine
| | - Petro Zhminko
- L.I. Medved's Research Center of Preventive Toxicology, Food and Chemical Safety, Kyiv, Ukraine
| | - Yana Kolianchuk
- L.I. Medved's Research Center of Preventive Toxicology, Food and Chemical Safety, Kyiv, Ukraine
| | - Nataliia Bubalo
- L.I. Medved's Research Center of Preventive Toxicology, Food and Chemical Safety, Kyiv, Ukraine
| | - Alex Odermatt
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland
| | - Martin Smieško
- Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland; Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland
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17
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Cheng Z, Hwang SS, Bhave M, Rahman T, Chee Wezen X. Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ. J Chem Inf Model 2023; 63:6912-6924. [PMID: 37883148 DOI: 10.1021/acs.jcim.3c01252] [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: 10/27/2023]
Abstract
Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
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Affiliation(s)
- Zixuan Cheng
- School of Engineering and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia
| | - Siaw San Hwang
- School of Engineering and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia
| | - Mrinal Bhave
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne 3122, Victoria, Australia
| | - Taufiq Rahman
- Department of Pharmacology, University of Cambridge, Cambridge CB2 1PD, U.K
| | - Xavier Chee Wezen
- School of Engineering and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia
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18
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Rodríguez-Belenguer P, March-Vila E, Pastor M, Mangas-Sanjuan V, Soria-Olivas E. Usage of model combination in computational toxicology. Toxicol Lett 2023; 389:34-44. [PMID: 37890682 DOI: 10.1016/j.toxlet.2023.10.013] [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: 09/08/2023] [Revised: 10/17/2023] [Accepted: 10/24/2023] [Indexed: 10/29/2023]
Abstract
New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.
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Affiliation(s)
- Pablo Rodríguez-Belenguer
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain; Department of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, 46100 Valencia, Spain
| | - Eric March-Vila
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, 46100 Valencia, Spain; Interuniversity Research Institute for Molecular Recognition and Technological Development, Universitat Politècnica de València, 46100 Valencia, Spain
| | - Emilio Soria-Olivas
- IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de València, 46100 Valencia, Spain.
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19
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Benny S, Rajappan Krishnendu P, Kumar S, Bhaskar V, Manisha DS, Abdelgawad MA, Ghoneim MM, Naguib IA, Pappachen LK, Mary Zachariah S, Mathew B, Tp A. A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling. J Biomol Struct Dyn 2023:1-20. [PMID: 37870113 DOI: 10.1080/07391102.2023.2270752] [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: 05/04/2023] [Accepted: 08/03/2023] [Indexed: 10/24/2023]
Abstract
Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sonu Benny
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Prayaga Rajappan Krishnendu
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Vaishnav Bhaskar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Deepthi S Manisha
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Mohamed A Abdelgawad
- Department of pharmaceutical chemistry, College of Pharmacy, Jouf University, Sakaka, Saudi Arabia
- Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt
| | - Mohammed M Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Saudi Arabia
| | - Ibrahim A Naguib
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, Taif, Saudi Arabia
| | - Leena K Pappachen
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Subin Mary Zachariah
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
| | - Aneesh Tp
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, Kerala, India
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20
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Yamari I, Abchir O, Mali SN, Errougui A, Talbi M, Kouali ME, Chtita S. The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation. SCIENTIFIC AFRICAN 2023; 21:e01754. [PMID: 37332393 PMCID: PMC10260260 DOI: 10.1016/j.sciaf.2023.e01754] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/31/2023] [Accepted: 06/08/2023] [Indexed: 06/20/2023] Open
Abstract
Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using 'PaDEL' and 'ChemDes' software, and then redundant and non-significant descriptors were eliminated by a module in 'QSARINS ver. 2.2.2'. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.
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Affiliation(s)
- Imane Yamari
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Oussama Abchir
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Suraj N Mali
- Department of Pharmaceutical Sciences & Technology, BIRLA INSTITUTE OF TECHNOLOGY (Mesra Campus), Mesra 835215, Jharkhand, India
| | - Abdelkbir Errougui
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Mohammed Talbi
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Mhammed El Kouali
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Samir Chtita
- Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
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21
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Spiers RC, Norby C, Kalivas JH. Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra. Anal Chem 2023; 95:12776-12784. [PMID: 37594455 DOI: 10.1021/acs.analchem.3c01616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Determining sample similarity underlies many foundational principles in analytical chemistry. For example, calibration models are unsuitable to predict outliers. Calibration transfer methods assume a moderate degree of sample and measurement dissimilarities between a calibration set and target prediction samples. Classification approaches link target sample similarities to groups of similar class samples. Although similarity is ubiquitous in analytical chemistry and everyday life, quantifying sample similarity is without a straightforward solution, especially when target domain samples are unlabeled and the only known features are measurable, such as spectra (the focus of this paper). The process proposed to assess sample similarity integrates spectral similarity information with contextual considerations among source analyte contents, model, and analyte predictions. This hybrid approach named the physicochemical responsive integrated similarity measure (PRISM) amplifies hidden-but-essential physicochemical properties encoded within respective spectra. PRISM is tested on four near-infrared (NIR) data sets for four diverse application areas to show efficacy. These applications are the assessment of prediction reliability and model updating for model generalizability, outlier detection, and basic matrix matching evaluation. Discussion is provided on adapting PRISM to classification problems. Results indicate that PRISM collects large amounts of similarity information and effectively integrates it to produce a quantitative similarity evaluation between the target sample and a source domain. The approach is also useful for biological samples with additional physiochemical variations. While PRISM is dynamically tested on NIR data, parts of PRISM were previously applied to other data types, and PRISM should be applicable to other measurement systems perturbed by matrix effects.
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Affiliation(s)
- Robert C Spiers
- Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States
| | - Callan Norby
- Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States
| | - John H Kalivas
- Department of Chemistry, Idaho State University, Pocatello, Idaho 83209, United States
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22
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Singh R, Kumar P, Sindhu J, Devi M, Kumar A, Lal S, Singh D, Kumar H. Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential. Future Med Chem 2023; 15:1273-1294. [PMID: 37551699 DOI: 10.4155/fmc-2023-0144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] Open
Abstract
Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
| | - Harish Kumar
- Department of Chemistry, School of Basic Sciences, Central University Haryana, Mahendergarh, 123029, India
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23
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Kalian AD, Benfenati E, Osborne OJ, Gott D, Potter C, Dorne JLCM, Guo M, Hogstrand C. Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity. TOXICS 2023; 11:572. [PMID: 37505541 PMCID: PMC10384850 DOI: 10.3390/toxics11070572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/29/2023]
Abstract
Dimensionality reduction techniques are crucial for enabling deep learning driven quantitative structure-activity relationship (QSAR) models to navigate higher dimensional toxicological spaces, however the use of specific techniques is often arbitrary and poorly explored. Six dimensionality techniques (both linear and non-linear) were hence applied to a higher dimensionality mutagenicity dataset and compared in their ability to power a simple deep learning driven QSAR model, following grid searches for optimal hyperparameter values. It was found that comparatively simpler linear techniques, such as principal component analysis (PCA), were sufficient for enabling optimal QSAR model performances, which indicated that the original dataset was at least approximately linearly separable (in accordance with Cover's theorem). However certain non-linear techniques such as kernel PCA and autoencoders performed at closely comparable levels, while (especially in the case of autoencoders) being more widely applicable to potentially non-linearly separable datasets. Analysis of the chemical space, in terms of XLogP and molecular weight, uncovered that the vast majority of testing data occurred within the defined applicability domain, as well as that certain regions were measurably more problematic and antagonised performances. It was however indicated that certain dimensionality reduction techniques were able to facilitate uniquely beneficial navigations of the chemical space.
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Affiliation(s)
- Alexander D Kalian
- Department of Nutritional Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford St., London SE1 9NH, UK
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | | | - David Gott
- Food Standards Agency, 70 Petty France, London SW1H 9EX, UK
| | - Claire Potter
- Food Standards Agency, 70 Petty France, London SW1H 9EX, UK
| | - Jean-Lou C M Dorne
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy
| | - Miao Guo
- Department of Engineering, King's College London, Strand Campus, Strand, London WC2R 2LS, UK
| | - Christer Hogstrand
- Department of Analytical, Environmental and Forensic Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford St., London SE1 9NH, UK
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24
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Rangel-Peña UJ, Zárate-Hernández LA, Camacho-Mendoza RL, Gómez-Castro CZ, González-Montiel S, Pescador-Rojas M, Meneses-Viveros A, Cruz-Borbolla J. Conceptual DFT, machine learning and molecular docking as tools for predicting LD 50 toxicity of organothiophosphates. J Mol Model 2023; 29:217. [PMID: 37380915 DOI: 10.1007/s00894-023-05630-4] [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: 03/21/2023] [Accepted: 06/21/2023] [Indexed: 06/30/2023]
Abstract
CONTEXT Several descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD50) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R2 values for the training set (R2Train) and R2 values for the test set (R2Test), around 0.90. METHODS The molecular structure of all organothiophosphates was optimized via the range-separated hybrid functional ωB97XD with the 6-311 + + G** basis set. Seven hundred and eighty-seven descriptors have been processed using a variety of machine learning algorithms: RF LASSO, Ridge, EN and SVM to generate a predictive model. The properties were obtained with Multiwfn, AIMALL and VMD programs. Docking simulations were performed by using AutoDock 4.2 and LigPlot + programs. All the calculations in this work are carried out in Gaussian 16 program package.
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Affiliation(s)
- Uriel J Rangel-Peña
- Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México
| | - Luis A Zárate-Hernández
- Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México
| | - Rosa L Camacho-Mendoza
- Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México
| | - Carlos Z Gómez-Castro
- Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México
| | - Simplicio González-Montiel
- Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México
| | | | - Amilcar Meneses-Viveros
- Departamento de Computación, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, Ciudad de Mexico, 07360, México
| | - Julián Cruz-Borbolla
- Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México.
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25
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Handa K, Sakamoto S, Kageyama M, Iijima T. Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors. Eur J Drug Metab Pharmacokinet 2023:10.1007/s13318-023-00832-w. [PMID: 37266860 DOI: 10.1007/s13318-023-00832-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (Kp) is a key parameter for the PBPK model to describe differential equations. However, it is difficult to obtain the Kp value experimentally because the measurement of drug concentration in the tissue is much harder than that in plasma. OBJECTIVE Instead of experiments, many researchers have sought in silico methods. Today, most of the models for Kp prediction are using in vitro and in vivo parameters as explanatory variables. We thought of physicochemical descriptors that could improve the predictability. Therefore, we aimed to develop the two-dimensional quantitative structure-activity relationship (2D-QSAR) model for Kp using physicochemical descriptors instead of in vivo experimental data as explanatory variables. METHODS We compared our model with the conventional models using 20-fold cross-validation according to the published method (Yun et al. J Pharmacokinet Pharmacodyn 41:1-14, 2014). We used random forest algorithm, which is known to be one of the best predictors for the 2D-QSAR model. Finally, we combined minimum in vitro experimental values and physiochemical descriptors. Thus, the prediction method for Kp value using a few in vitro parameters and physicochemical descriptors was developed; this is a multimodal model. RESULTS Its accuracy was found to be superior to that of the conventional models. Results of this research suggest that multimodality is useful for the 2D-QSAR model [RMSE and % of two-fold error: 0.66 and 42.2% (Berezohkovsky), 0.52 and 52.2% (Rodgers), 0.65 and 34.6% (Schmitt), 0.44 and 61.1% (published model), 0.41 and 62.1% (traditional model), 0.39 and 64.5% (multimodal model)]. CONCLUSION We could develop a 2D-QSAR model for Kp value with the highest accuracy using a few in vitro experimental data and physicochemical descriptors.
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Affiliation(s)
- Koichi Handa
- Toxicology & DMPK Research Department, Teijin Institute for Bio-Medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo, 191-8512, Japan.
| | - Seishiro Sakamoto
- Pharmaceutical Development Coordination Department, Teijin Pharma Limited, 3-2-1, Kasumigaseki Common Gate West Tower, Kasumigaseki Chiyoda-ku, Tokyo, 100-8585, Japan
| | - Michiharu Kageyama
- Toxicology & DMPK Research Department, Teijin Institute for Bio-Medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo, 191-8512, Japan
| | - Takeshi Iijima
- Toxicology & DMPK Research Department, Teijin Institute for Bio-Medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo, 191-8512, Japan
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26
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Janet JP, Mervin L, Engkvist O. Artificial intelligence in molecular de novo design: Integration with experiment. Curr Opin Struct Biol 2023; 80:102575. [PMID: 36966692 DOI: 10.1016/j.sbi.2023.102575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 06/04/2023]
Abstract
In this mini review, we capture the latest progress of applying artificial intelligence (AI) techniques based on deep learning architectures to molecular de novo design with a focus on integration with experimental validation. We will cover the progress and experimental validation of novel generative algorithms, the validation of QSAR models and how AI-based molecular de novo design is starting to become connected with chemistry automation. While progress has been made in the last few years, it is still early days. The experimental validations conducted thus far should be considered proof-of-principle, providing confidence that the field is moving in the right direction.
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Affiliation(s)
- Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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27
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Yuan B, Wang Y, Zong C, Sang L, Chen S, Liu C, Pan Y, Zhang H. Modeling study for predicting altered cellular activity induced by nanomaterials based on Dlk1-Dio3 gene expression and structural relationships. CHEMOSPHERE 2023; 335:139090. [PMID: 37268226 DOI: 10.1016/j.chemosphere.2023.139090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
Nanomaterials have been widely applied and developed due to its unique physicochemical characteristics, such as their small size. The environmental and biological effects caused by nanomaterials have raised concerns. In particular, some nanometal oxides have obvious biological toxicity and pose a major safety problem. The prediction model established by combining the expression levels of key genes with quantitative structure-activity relationship (QSAR) studies can predict the biotoxicity of nanomaterials by relying on both structural information and gene regulation information. This model can fill the gap of missing mechanisms in QSAR studies. In this study, we exposed A549 cells and BEAS-2B cells to 21 nanometal oxides for 24 h. Cell viability was assessed by measuring absorbance values using the CCK8 assay, and the expression levels of the Dlk1-Dio3 gene cluster were measured. By using the theoretical basis of the nano-QSAR model and the improved principles of the SMILES-based descriptors to combine specific gene expression and structural factors, new models were constructed using Monte Carlo partial least squares (MC-PLS) for the biotoxicity of the nanometal oxides on two different lung cells. The overall quality of the nano-QSAR models constructed by combining specific gene expression and structural parameters for A549 and BEAS-2B cells was better than that of the models constructed based on structural parameters only. The coefficient of determination (R2) of the A549 cell model increased from 0.9044 to 0.9969, and the Root Mean Square Error (RMSE) decreased from 0.1922 to 0.0348. The R2 of the BEAS-2B cell model increased from 0.9355 to 0.9705, and the RMSE decreased from 0.1206 to 0.0874. The model validation proved the proposed models have a good prediction, generalization ability and model stability. This study offers a new research perspective for the toxicity assessment of nanometal oxides, contributing to a more systematic safety evaluation of nanomaterials.
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Affiliation(s)
- Beilei Yuan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Yunlin Wang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Cheng Zong
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Leqi Sang
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Shuang Chen
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Chengzhi Liu
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Yong Pan
- College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China.
| | - Huazhong Zhang
- Department of Emergency Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
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28
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Meneses J, González-Durruthy M, Fernandez-de-Gortari E, Toropova AP, Toropov AA, Alfaro-Moreno E. A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data. Part Fibre Toxicol 2023; 20:21. [PMID: 37211608 DOI: 10.1186/s12989-023-00530-0] [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: 02/02/2023] [Accepted: 05/01/2023] [Indexed: 05/23/2023] Open
Abstract
BACKGROUND The widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles. RESULTS Tree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R2 and Q2-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity. CONCLUSIONS The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.
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Affiliation(s)
- João Meneses
- NanoSafety Group, International Iberian Nanotechnology Laboratory, Braga, 4715-330, Portugal
| | | | | | - Alla P Toropova
- Instituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, 20156, Italy
| | - Andrey A Toropov
- Instituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milano, 20156, Italy
| | - Ernesto Alfaro-Moreno
- NanoSafety Group, International Iberian Nanotechnology Laboratory, Braga, 4715-330, Portugal.
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Goyal S, Rani P, Chahar M, Hussain K, Kumar P, Sindhu J. Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies. J Biomol Struct Dyn 2023; 41:13616-13631. [PMID: 37010991 DOI: 10.1080/07391102.2023.2193991] [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: 10/11/2022] [Accepted: 02/03/2023] [Indexed: 04/04/2023]
Abstract
Endocrine disrupter chemicals (EDCs) are both natural and man-made chemicals that mimic, block or interfere with human hormonal system. In the present manuscript, QSAR modeling was performed for the androgen disruptors that interfere with biosynthesis, metabolism or action of androgens that causes adverse effects on male reproductive system. A set of 96 EDCs that exhibited affinity towards androgen receptors (Log RBA) in rats were employed for carrying out QSAR studies using Hybrid descriptors (combination of HFG and SMILES) through Monte Carlo Optimization. Using index of ideality of correlation (TF2), five splits were formed and predictability of five models resulting from these splits was assessed by various validation parameters. Models resulted from first split was the top most one with R2validation = 0.7878. Structural attributes responsible for change in endpoint were studied by employing correlation weights of structural attributes. In order to further validate the model, new EDCs were designed using these attributes. In silico molecular modelling studies were performed to assess the detailed interactions with the receptor. The binding energies of all the designed compounds were observed to be better than lead and are in the range of -10.46 to -14.80. Molecular dynamics simulation of 100 ns was performed for ED01 and NED05. The results revealed that the protein-ligand complex bearing NED05 was more stable than lead ED01 exhibiting better interactions with the receptor. Further, in an attempt to assess their metabolism, ADME studies were evaluated using SwissADME. The developed model enables to predict the characteristics of designed compounds in an authentic way.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Surbhi Goyal
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Payal Rani
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Monika Chahar
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Khalid Hussain
- Department of AS&H, Mewat Engineering College, Palla, Nuh, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
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30
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Banerjee A, Roy K. On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points. Chem Res Toxicol 2023; 36:446-464. [PMID: 36811528 DOI: 10.1021/acs.chemrestox.2c00374] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
The novel quantitative read-across structure-activity relationship (q-RASAR) approach uses read-across-derived similarity functions in the quantitative structure-activity relationship (QSAR) modeling framework in a unique way for supervised model generation. The aim of this study is to explore how this workflow enhances the external (test set) prediction quality of conventional QSAR models by the incorporation of some novel similarity-based functions as additional descriptors using the same level of chemical information. To establish this, five different toxicity data sets, for which QSAR models were reported previously, have been considered in the q-RASAR modeling exercise, which uses chemical similarity-derived measures. The identical sets of chemical features along with the same compositions of training and test sets as reported previously were used in the present analysis for ease of comparison. The RASAR descriptors were calculated based on a chosen similarity measure with the default setting of relevant hyperparameter(s) and were then clubbed with the original structural and physicochemical descriptors, and the number of selected features was further optimized by employing a grid search technique applied on the respective training sets. These features were then used to develop multiple linear regression (MLR) q-RASAR models that show enhanced predictivity as compared to the QSAR models developed previously. Moreover, various other ML algorithms like support vector machine (SVM), linear SVM, random forest, partial least squares, and ridge regression were also employed using the same feature combinations as used in the MLR models to compare the prediction qualities. The q-RASAR models for five different data sets possess at least one of the RASAR descriptors, RA function, gm, and average similarity, suggesting that these are important determinants of similarities that contribute to the development of predictive q-RASAR models, as also evident from the SHAP analysis of the models.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
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31
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Singh R, Kumar P, Sindhu J, Devi M, Kumar A, Lal S, Singh D. Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies. Comput Biol Med 2023; 157:106776. [PMID: 36947906 DOI: 10.1016/j.compbiomed.2023.106776] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/20/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
α-Amylase (EC.3.2.1.1) is a ubiquitous digestive endoamylase. The abrupt rise in blood glucose levels due to the hydrolysis of carbohydrates by α-amylase at a faster rate is one of the main reasons for type 2 diabetes. The inhibitors prevent the action of digestive enzymes, slowing the digestion of carbs and eventually assisting in the management of postprandial hyperglycemia. In the course of developing α-amylase inhibitors, we have screened 2-aryliminothiazolidin-4-one based analogs for their in vitro α-amylase inhibitory potential and employed various in silico approaches for the detailed exploration of the bioactivity. The DNSA bioassay revealed that compounds 5c, 5e, 5h, 5j, 5m, 5o and 5t were more potent than the reference drug (IC60 value = 22.94 ± 0.24 μg mL-1). The derivative 5o with -NO2 group at both the rings was the most potent analog with an IC60 value of 19.67 ± 0.20 μg mL-1 whereas derivative 5a with unsubstituted aromatic rings showed poor inhibitory potential with an IC60 value of 33.40 ± 0.15 μg mL-1. The reliable QSAR models were developed using the QSARINS software. The high value of R2ext = 0.9632 for model IM-9 showed that the built model can be applied to predict the α-amylase inhibitory activity of the untested molecules. A consensus modelling approach was also employed to test the reliability and robustness of the developed QSAR models. Molecular docking and molecular dynamics were employed to validate the bioassay results by studying the conformational changes and interaction mechanisms. A step further, these compounds also exhibited good ADMET characteristics and bioavailability when tested for in silico pharmacokinetics prediction parameters.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India.
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
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Ganji M, Bakhshi S, Shoari A, Ahangari Cohan R. Discovery of potential FGFR3 inhibitors via QSAR, pharmacophore modeling, virtual screening and molecular docking studies against bladder cancer. J Transl Med 2023; 21:111. [PMID: 36765337 PMCID: PMC9913026 DOI: 10.1186/s12967-023-03955-5] [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: 10/29/2022] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Fibroblast growth factor receptor 3 is known as a favorable aim in vast range of cancers, particularly in bladder cancer treatment. Pharmacophore and QSAR modeling approaches are broadly utilized for developing novel compounds for the determination of inhibitory activity versus the biological target. In this study, these methods employed to identify FGFR3 potential inhibitors. METHODS To find the potential compounds for bladder cancer targeting, ZINC and NCI databases were screened. Pharmacophore and QSAR modeling of FGFR3 inhibitors were utilized for dataset screening. Then, with regard to several factors such as Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties and Lipinski's Rule of Five, the recognized compounds were filtered. In further step, utilizing the flexible docking technique, the obtained compounds interactions with FGFR3 were analyzed. RESULTS The best five compounds, namely ZINC09045651, ZINC08433190, ZINC00702764, ZINC00710252 and ZINC00668789 were selected for Molecular Dynamics (MD) studies. Off-targeting of screened compounds was also investigated through CDD search and molecular docking. MD outcomes confirmed docking investigations and revealed that five selected compounds could make steady interactions with the FGFR3 and might have effective inhibitory potencies on FGFR3. CONCLUSION These compounds can be considered as candidates for bladder cancer therapy with improved therapeutic properties and less adverse effects.
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Affiliation(s)
- Mahmoud Ganji
- grid.412266.50000 0001 1781 3962Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Shohreh Bakhshi
- grid.411705.60000 0001 0166 0922Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Shoari
- grid.420169.80000 0000 9562 2611Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Reza Ahangari Cohan
- Department of Nanobiotechnology, New Technologies Research Group, Pasteur Institute of Iran, No. 69, Pasteur Ave, Tehran, 1316543551, Iran.
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Zothantluanga JH, Chetia D, Rajkhowa S, Umar AK. Unsupervised machine learning, QSAR modelling and web tool development for streamlining the lead identification process of antimalarial flavonoids. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:117-146. [PMID: 36744427 DOI: 10.1080/1062936x.2023.2169347] [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: 10/31/2022] [Accepted: 01/10/2023] [Indexed: 06/18/2023]
Abstract
Identification of lead compounds with the traditional laboratory approach is expensive and time-consuming. Nowadays, in silico techniques have emerged as a promising approach for lead identification. In this study, we aim to develop robust and predictive 2D-QSAR models to identify lead flavonoids by predicting the IC50 against Plasmodium falciparum. We applied machine learning algorithms (Principal component analysis followed by K-means clustering) and Pearson correlation analysis to select 9 molecular descriptors (MDs) for model building. We selected and validated the three best QSAR models after execution of multiple linear regression (MLR) 100 times with different combinations of MDs. The developed models have fulfilled the five principles for QSAR models as specified by the Organization for Economic Co-operation and Development. The outcome of the study is a reliable and sustainable in silico method of IC50 (Mean ± SD) prediction that will positively impact the antimalarial drug development process by reducing the money and time required to identify potential antimalarial lead compounds from the class of flavonoids. We also developed a web tool (JazQSAR, https://etflin.com/news/4) to offer an easily accessible platform for the developed QSAR models.
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Affiliation(s)
- J H Zothantluanga
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
| | - D Chetia
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
| | - S Rajkhowa
- Centre for Biotechnology and Bioinformatics, Faculty of Biological Sciences, Dibrugarh University, Dibrugarh, India
| | - A K Umar
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
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Ballesteros-Casallas A, Quiroga C, Ortiz C, Benítez D, Denis PA, Figueroa D, Salas CO, Bertrand J, Tapia RA, Sánchez P, Miscione GP, Comini MA, Paulino M. Mode of action of p-quinone derivatives with trypanocidal activity studied by experimental and in silico models. Eur J Med Chem 2023; 246:114926. [PMID: 36508970 DOI: 10.1016/j.ejmech.2022.114926] [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: 09/16/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022]
Abstract
Quinones are attractive pharmacological scaffolds for developing new agents for the treatment of different transmissible and non-transmissible human diseases due to their capacity to alter the cell redox homeostasis. The bioactivity and potential mode of action of 19 p-quinone derivatives fused to different aromatic rings (carbo or heterocycles) and harboring distinct substituents were investigated in infective Trypanosoma brucei brucei. All the compounds, except for a furanequinone (EC50=38 μM), proved to be similarly or even more potent (EC50 = 0.5-5.5 μM) than the clinical drug nifurtimox (EC50 = 5.3 μM). Three furanequinones and one thiazolequinone displayed a higher selectivity than nifurtimox. Two of these selective hits resulted potent inhibitors of T. cruzi proliferation (EC50=0.8-1.1 μM) but proved inactive against Leishmania infantum amastigotes. Most of the p-quinones induced a rapid and marked intracellular oxidation in T. b. brucei. DFT calculations on the oxidized quinone (Q), semiquinone (Q•-) and hydroquinone (QH2) suggest that all quinones have negative ΔG for the formation of Q•-. Qualitative and quantitative structure-activity relationship analyses in two or three dimensions of different electronic and biophysical descriptors of quinones and their corresponding bioactivities (killing potency and oxidative capacity) were performed. Charge distribution over the quinone ring carbons of Q and Q.- and the frontier orbitals energies of SUMO (Q.-) and LUMO (Q) correlate with their oxidative and trypanocidal activity. QSAR analysis also highlighted that both bromine substitution in the p-quinone ring and a bulky phenyl group attached to the furane and thiazole rings (which generates a negative charge due to the π electron system polarized by the nearby heteroatoms) are favorable for activity. By combining experimental and in silico procedures, this study disclosed important information about p-quinones that may help to rationally tune their electronic properties and biological activities.
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Affiliation(s)
- Andres Ballesteros-Casallas
- COBO, Computational Bio-Organic Chemistry, Chemistry Department, Universidad de Los Andes, Carrera 1 18A-12, Bogotá, 111711, Colombia; Bioinformatics Center, DETEMA Department, Faculty of Chemistry, Universidad de la República, General Flores 2124, Montevideo, 11600, Uruguay
| | - Cristina Quiroga
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay
| | - Cecilia Ortiz
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay
| | - Diego Benítez
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay
| | - Pablo A Denis
- Computational Nanotechnology, DETEMA Department, Faculty of Chemistry, Universidad de la República, General Flores 2124, Montevideo, 11600, Uruguay
| | - David Figueroa
- COBO, Computational Bio-Organic Chemistry, Chemistry Department, Universidad de Los Andes, Carrera 1 18A-12, Bogotá, 111711, Colombia
| | - Cristian O Salas
- Departamento de Química Orgánica, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, 6094411, Chile
| | - Jeanluc Bertrand
- Departamento de Química Orgánica, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, 6094411, Chile
| | - Ricardo A Tapia
- Departamento de Química Orgánica, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, 6094411, Chile
| | - Patricio Sánchez
- Departamento de Química Orgánica, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago, 6094411, Chile
| | - Gian Pietro Miscione
- COBO, Computational Bio-Organic Chemistry, Chemistry Department, Universidad de Los Andes, Carrera 1 18A-12, Bogotá, 111711, Colombia.
| | - Marcelo A Comini
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay.
| | - Margot Paulino
- Bioinformatics Center, DETEMA Department, Faculty of Chemistry, Universidad de la República, General Flores 2124, Montevideo, 11600, Uruguay.
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How the Structure of Per- and Polyfluoroalkyl Substances (PFAS) Influences Their Binding Potency to the Peroxisome Proliferator-Activated and Thyroid Hormone Receptors-An In Silico Screening Study. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020479. [PMID: 36677537 PMCID: PMC9866891 DOI: 10.3390/molecules28020479] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 01/06/2023]
Abstract
In this study, we investigated PFAS (per- and polyfluoroalkyl substances) binding potencies to nuclear hormone receptors (NHRs): peroxisome proliferator-activated receptors (PPARs) α, β, and γ and thyroid hormone receptors (TRs) α and β. We have simulated the docking scores of 43 perfluoroalkyl compounds and based on these data developed QSAR (Quantitative Structure-Activity Relationship) models for predicting the binding probability to five receptors. In the next step, we implemented the developed QSAR models for the screening approach of a large group of compounds (4464) from the NORMAN Database. The in silico analyses indicated that the probability of PFAS binding to the receptors depends on the chain length, the number of fluorine atoms, and the number of branches in the molecule. According to the findings, the considered PFAS group bind to the PPARα, β, and γ only with low or moderate probability, while in the case of TR α and β it is similar except that those chemicals with longer chains show a moderately high probability of binding.
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Zothantluanga JH, Umar AK, Lalhlenmawia H, Vinayagam S, Borthakur MS, Patowary L, Tayeng D. Computational screening of phytochemicals for anti-parasitic drug discovery. PHYTOCHEMISTRY, COMPUTATIONAL TOOLS AND DATABASES IN DRUG DISCOVERY 2023:257-283. [DOI: 10.1016/b978-0-323-90593-0.00005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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37
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Hentabli H, Bengherbia B, Saeed F, Salim N, Nafea I, Toubal A, Nasser M. Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction. Int J Mol Sci 2022; 23:13230. [PMID: 36362018 PMCID: PMC9657591 DOI: 10.3390/ijms232113230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/22/2022] [Accepted: 10/27/2022] [Indexed: 10/15/2023] Open
Abstract
Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds' bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBFN.
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Affiliation(s)
- Hamza Hentabli
- Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria
- UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Billel Bengherbia
- Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria
| | - Faisal Saeed
- UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
- DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
| | - Naomie Salim
- UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Ibtehal Nafea
- College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia
| | - Abdelmoughni Toubal
- Laboratory of Advanced Electronics Systems (LSEA), University of Medea, Medea 26000, Algeria
| | - Maged Nasser
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
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Li J, Wang C, Yue L, Chen F, Cao X, Wang Z. Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 243:113955. [PMID: 35961199 DOI: 10.1016/j.ecoenv.2022.113955] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/11/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.
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Affiliation(s)
- Jing Li
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Chuanxi Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Le Yue
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Feiran Chen
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment and Civil Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Engineering Laboratory for Biomass Energy and Carbon Reduction Technology, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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Kumar P, Singh R, Kumar A, Toropova AP, Toropov AA, Devi M, Lal S, Sindhu J, Singh D. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:677-700. [PMID: 36093620 DOI: 10.1080/1062936x.2022.2120068] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - R Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - A P Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - M Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - S Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - J Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - D Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India
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40
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A Discovery Strategy for Active Compounds of Chinese Medicine Based on the Prediction Model of Compound-Disease Relationship. JOURNAL OF ONCOLOGY 2022; 2022:8704784. [PMID: 35847368 PMCID: PMC9286898 DOI: 10.1155/2022/8704784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 06/16/2022] [Indexed: 11/17/2022]
Abstract
An accurate characterization of diseases and compounds is the key to predicting the compound-disease relationship (CDR). However, due to the difficulty of a comprehensive description of CDR, the accuracy of traditional drug development models for large-scale CDR prediction is usually unsatisfactory. In order to solve this problem, we propose a new method that integrates the molecular descriptors of compounds and the symptom descriptors of diseases to build a CDR two-dimensional matrix to predict candidate active compounds. The Matlab software draws grayscale images of CDRs, which are used as a benchmark dataset for training convolutional neural network (CNN) models. The trained model is used to predict candidate antitumor active compounds. Among the AlexNet and GoogLeNet models, we selected the GoogLeNet model for the prediction of active compounds in Chinese medicine, and its Acc, Sen, Pre, F-measure, MCC, and AUC are 0.960, 0.956, 0.965, 0.960, 0.920, and 0.964, respectively. In the prediction results of compounds, 1624 candidate CDRs were found in 124 Chinese medicines. Among them, we obtained 31 features of candidate antitumor active compounds. This method provides new insights for the discovery of candidate active compounds in Chinese medicine.
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41
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De P, Kumar V, Kar S, Roy K, Leszczynski J. Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection. Struct Chem 2022; 33:1741-1753. [PMID: 35692512 PMCID: PMC9171098 DOI: 10.1007/s11224-022-01975-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022]
Abstract
The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure–activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the “Prediction Reliability Indicator” tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates.
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Affiliation(s)
- Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217 USA
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217 USA
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Singh R, Kumar P, Devi M, Lal S, Kumar A, Sindhu J, Toropova AP, Toropov AA, Singh D. Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors. NEW J CHEM 2022. [DOI: 10.1039/d2nj03515d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Monte Carlo optimization based QSGFEAR model development using CII results in the formation of more reliable, robust and predictive models.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra-136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Alla P. Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Andrey A. Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
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