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Kumar S, Ali I, Abbas F, Shafiq F, Yadav AK, Ghate MD, Kumar D. In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer. Mol Divers 2024:10.1007/s11030-024-10817-9. [PMID: 38470555 DOI: 10.1007/s11030-024-10817-9] [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: 08/14/2023] [Accepted: 01/25/2024] [Indexed: 03/14/2024]
Abstract
Globally, lung cancer is a significant public health concern due to its role as the leading cause of cancer-related mortalities. The promising target of EGFR for lung cancer treatment has been identified, providing a potential avenue for more effective therapies. The purpose of the study was to design a library of 1843 coumarin-1,2,3-triazole hybrids and screen them based on a designed pharmacophore to identify potential inhibitors targeting EGFR in lung cancer with minimum or no side effects. Pharmacophore-based screening was carried out and 60 hits were obtained. To gain a better understanding of the binding interactions between the compounds and the targeted receptor, molecular docking was conducted on the 60 screened compounds. In-silico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results indicated that coumarin-1,2,3-triazole hybrids COUM-0849, COUM-0935, COUM-0414, COUM-1335, COUM-0276, and COUM-0484 exhibit dock score of - 10.2, - 10.2, - 10.1, - 10.1, - 10, - 10 while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, we performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of coumarin-1,2,3-triazole hybrids as promising EGFR inhibitors for the management of lung cancer.
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Affiliation(s)
- Sunil Kumar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Iqra Ali
- Department of Biosciences, COMSATS University Islamabad, Islamabad Campus, Islamabad, 45550, Pakistan
| | - Faheem Abbas
- Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Faiza Shafiq
- Department of Chemistry, University of Agriculture, Faisalabad, 38040, Pakistan
| | - Ashok Kumar Yadav
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh, 160014, India
| | - Manjunath D Ghate
- School of Pharmacy, National Forensic Sciences University, Gandhinagar, Gujarat, 382007, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan, Himachal Pradesh, 173229, India.
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Kumar S, Ali I, Abbas F, Rana A, Pandey S, Garg M, Kumar D. In-silico design, pharmacophore-based screening, and molecular docking studies reveal that benzimidazole-1,2,3-triazole hybrids as novel EGFR inhibitors targeting lung cancer. J Biomol Struct Dyn 2023:1-23. [PMID: 37646177 DOI: 10.1080/07391102.2023.2252496] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/18/2023] [Indexed: 09/01/2023]
Abstract
Lung cancer is a complex and heterogeneous disease, which has been associated with various molecular alterations, including the overexpression and mutations of the epidermal growth factor receptor (EGFR). In this study, designed a library of 1843 benzimidazole-1,2,3-triazole hybrids and carried out pharmacophore-based screening to identify potential EGFR inhibitors. The 164 compounds were further evaluated using molecular docking and molecular dynamics simulations to understand the binding interactions between the compounds and the receptor. In-si-lico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results of this study indicate that benzimidazole-1,2,3-triazole hybrids BENZI-0660, BENZI-0125, BENZI-0279, BENZI-0415, BENZI-0437, and BENZI-1110 exhibit dock scores of -9.7, -9.6, -9.6, -9.6, -9.6, -9.6 while referencing molecule -7.9 kcal/mol for EGFR (PDB ID: 4HJO), respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzimidazole-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. This research opens up a new avenue for the discovery and development of potent and selective EGFR inhibitors for the treatment of lung cancer.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sunil Kumar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan, India
| | - Iqra Ali
- Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan
| | - Faheem Abbas
- Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing, P. R. China
| | - Anurag Rana
- Yogananda School of Artificial Intelligence, Computers, and Data Sciences, Shoolini University, Solan, India
| | - Sadanand Pandey
- Department of Chemistry, College of Natural Science, Yeungnam University, Gyeongsan, Korea
| | - Manoj Garg
- Amity Institute of Molecular Medicine and Stem Cell Research, Amity University, Noida, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan, India
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Rahman S, Nath S, Mohan U, Das AK. Targeting Staphylococcal Cell-Wall Biosynthesis Protein FemX Through Steered Molecular Dynamics and Drug-Repurposing Approach. ACS OMEGA 2023; 8:29292-29301. [PMID: 37599983 PMCID: PMC10433341 DOI: 10.1021/acsomega.3c02691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/24/2023] [Indexed: 08/22/2023]
Abstract
Staphylococcus aureus-mediated infection is a serious threat in this antimicrobial-resistant world. S. aureus has become a "superbug" by challenging conventional as well as modern treatment strategies. Nowadays, drug repurposing has become a new trend for the discovery of new drug molecules. This study focuses on evaluating FDA-approved drugs that can be repurposed against S. aureus infection. Steered molecular dynamics (SMD) has been performed for Lumacaftor and Olaparib against staphylococcal FemX to understand their binding to the active site. A time-dependent external force or rupture force has been applied to the ligands to calculate the force required to dislocate the ligand from the binding pocket. SMD analysis indicates that Lumacaftor has a high affinity for the substrate binding pocket in comparison to Olaparib. Umbrella sampling exhibits that Lumacaftor possesses a higher free energy barrier to displace it from the ligand-binding site. The bactericidal activity of Lumacaftor and Olaparib has been tested, and it shows that Lumacaftor has moderate activity along with biofilm inhibition potential (MIC value with conc. 128 μg/mL). Pharmacokinetic and toxicology evaluations indicate that Lumacaftor has higher pharmacokinetic potential with lower toxicity. This is the first experimental report where staphylococcal FemX has been targeted for the discovery of new drugs. It is suggested that Lumacaftor may be a potential lead molecule against S. aureus.
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Affiliation(s)
- Shakilur Rahman
- Department
of Biotechnology, Indian Institute of Technology
Kharagpur, Kharagpur, West Bengal 721302, India
| | - Subham Nath
- National
Institute of Pharmaceutical Education and Research Kolkata, Kolkata, West Bengal 700054, India
| | - Utpal Mohan
- National
Institute of Pharmaceutical Education and Research Kolkata, Kolkata, West Bengal 700054, India
| | - Amit Kumar Das
- Department
of Biotechnology, Indian Institute of Technology
Kharagpur, Kharagpur, West Bengal 721302, India
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Kumar S, Ali I, Abbas F, Khan N, Gupta MK, Garg M, Kumar S, Kumar D. In-silico identification of small molecule benzofuran-1,2,3-triazole hybrids as potential inhibitors targeting EGFR in lung cancer via ligand-based pharmacophore modeling and molecular docking studies. In Silico Pharmacol 2023; 11:20. [PMID: 37575679 PMCID: PMC10412522 DOI: 10.1007/s40203-023-00157-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 07/26/2023] [Indexed: 08/15/2023] Open
Abstract
Lung cancer is one of the most common and deadly types of cancer worldwide, and the epidermal growth factor receptor (EGFR) has emerged as a promising therapeutic target for the treatment of this disease. In this study, we designed a library of 1840 benzofuran-1,2,3-triazole hybrids and conducted pharmacophore-based screening to identify potential EGFR inhibitors. The 20 identified compounds were further evaluated using molecular docking and molecular dynamics simulations to understand their binding interactions with the EGFR receptor. In-silico ADME and toxicity studies were also performed to assess their drug-likeness and safety profiles. The results of this study showed the benzofuran-1,2,3-triazole hybrids BENZ-0454, BENZ-0143, BENZ-1292, BENZ-0335, BENZ-0332, and BENZ-1070 dock score of - 10.2, - 10, - 9.9, - 9.8, - 9.7, - 9.6, while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of the receptor, indicating their potential as inhibitors. The in-silico ADME and toxicity studies suggested that the compounds had good pharmacokinetic and safety profiles, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzofuran-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. Overall, this study provides a valuable starting point for the development of novel EGFR inhibitors with improved efficacy and safety profiles. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s40203-023-00157-1.
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Affiliation(s)
- Sunil Kumar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan, Himachal Pradesh 173229 India
| | - Iqra Ali
- Department of Biosciences, COMSATS University Islamabad, Islamabad Campus, Islamabad, 45550 Pakistan
| | - Faheem Abbas
- Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing, 100084 People’s Republic of China
| | - Nimra Khan
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190 People’s Republic of China
| | - Manoj K. Gupta
- Department of Chemistry, School of Basic Sciences, Central University of Haryana, Mahendergarh, H.R. 123031 India
| | - Manoj Garg
- Amity Institute of Molecular Medicine and Stem Cell Research, Amity University UP, Sector-125, Noida, 201313 India
| | - Saroj Kumar
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan, Himachal Pradesh 173229 India
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Fine-tuning BERT for automatic ADME semantic labeling in FDA drug labeling to enhance product-specific guidance assessment. J Biomed Inform 2023; 138:104285. [PMID: 36632860 DOI: 10.1016/j.jbi.2023.104285] [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: 06/25/2022] [Revised: 10/25/2022] [Accepted: 01/07/2023] [Indexed: 01/11/2023]
Abstract
Product-specific guidances (PSGs) recommended by the United States Food and Drug Administration (FDA) are instrumental to promote and guide generic drug product development. To assess a PSG, the FDA assessor needs to take extensive time and effort to manually retrieve supportive drug information of absorption, distribution, metabolism, and excretion (ADME) from the reference listed drug labeling. In this work, we leveraged the state-of-the-art pre-trained language models to automatically label the ADME paragraphs in the pharmacokinetics section from the FDA-approved drug labeling to facilitate PSG assessment. We applied a transfer learning approach by fine-tuning the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to develop a novel application of ADME semantic labeling, which can automatically retrieve ADME paragraphs from drug labeling instead of manual work. We demonstrate that fine-tuning the pre-trained BERT model can outperform conventional machine learning techniques, achieving up to 12.5% absolute F1 improvement. To our knowledge, we were the first to successfully apply BERT to solve the ADME semantic labeling task. We further assessed the relative contribution of pre-training and fine-tuning to the overall performance of the BERT model in the ADME semantic labeling task using a series of analysis methods, such as attention similarity and layer-based ablations. Our analysis revealed that the information learned via fine-tuning is focused on task-specific knowledge in the top layers of the BERT, whereas the benefit from the pre-trained BERT model is from the bottom layers.
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Ashraf C, Joshi N, Beck DAC, Pfaendtner J. Data Science in Chemical Engineering: Applications to Molecular Science. Annu Rev Chem Biomol Eng 2021; 12:15-37. [PMID: 33710940 DOI: 10.1146/annurev-chembioeng-101220-102232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.
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Affiliation(s)
- Chowdhury Ashraf
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; ,
| | - Nisarg Joshi
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; ,
| | - David A C Beck
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; , .,eScience Institute, University of Washington, Seattle, Washington 98195, USA
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; ,
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Lumley JA, Desai P, Wang J, Cahya S, Zhang H. The Derivation of a Matched Molecular Pairs Based ADME/Tox Knowledge Base for Compound Optimization. J Chem Inf Model 2020; 60:4757-4771. [PMID: 32975944 DOI: 10.1021/acs.jcim.0c00583] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Matched Molecular Pairs (MMP) analysis is a well-established technique for Structure Activity and Property Analysis (SAR and SPR). Summarizing multiple MMPs that describe the same structural change into a single chemical transform can be a powerful tool for prediction (termed Transform from here on). This is particularly useful in the area of Absorption, Distribution, Metabolism, and Elimination (ADME) analysis that is less influenced by 3D structural binding effects. The creation of a knowledge database containing many of these Transforms across typical ADME assays promises to be a powerful approach to aid multidimensional optimization. We present a detailed workflow for the derivation of such a database. We include details of an MMP fragmentation algorithm with associated statistical summarization methods for the derivation of Transforms. This is made freely available as part of the LillyMol software package. We describe the application of this method to several ADME/Tox (Toxicity) assay data sets and highlight multiple cases where the impact of traditional medicinal chemistry Transforms is contradicted by MMP data. We also describe the internal software interface used by medicinal chemists to aid the design of new compounds via automated suggestion. This approach utilizes the matched pairs database to "suggest" improved compounds in an automated design scenario. A nonvisual script-based version of the automated suggestions code with an associated set of described chemical Transforms is also made freely available along with this paper and as part of the LillyMol software package. Finally, we contrast this knowledge database against a larger database of all MMPs derived from a 2 million compound diversity set and a subset of MMPs seen in historical discovery projects. The comparison against all transforms in the diversity collection highlights the very low coverage of the transform database as compared to all possible transforms involving 15 atom fragments. The comparison against a smaller subset of Transforms seen on internal Medicinal Chemistry projects shows better coverage of the transform database for a small set of common medicinal chemistry strategies. Within the context of all possible transforms available to a medicinal chemistry project team, the challenge remains to move beyond mere idea generation from past projects toward high quality prediction for novel ADME/Tox modulating Transforms.
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Affiliation(s)
- James A Lumley
- Data Science and Engineering, Lilly Research Laboratories, Eli Lilly and Company, Erl Wood Manor, Windlesham, Surrey GU20 6PH, United Kingdom
| | - Prashant Desai
- Computational ADME, ADME-Toxicology-PKPD, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jibo Wang
- Discovery Chemistry Research Technologies, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Suntara Cahya
- Discovery Statistics, Lilly Biotechnology Center, Eli Lilly and Company, San Diego, California 92121, United States
| | - Hongzhou Zhang
- Data Science and Engineering, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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Hu B, Zhou X, Mohutsky MA, Desai PV. Structure–Property Relationships and Machine Learning Models for Addressing CYP3A4-Mediated Victim Drug–Drug Interaction Risk in Drug Discovery. Mol Pharm 2020; 17:3600-3608. [DOI: 10.1021/acs.molpharmaceut.0c00637] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Bingjie Hu
- Computational ADME, ADME−Toxicology−PKPD, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Xin Zhou
- ADME−Toxicology−PKPD, Lilly Research Laboratories, Eli Lilly and Company, San Diego, California 92121, United States
| | - Michael A. Mohutsky
- Investigational Drug Disposition, ADME−Toxicology−PKPD, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Prashant V. Desai
- Computational ADME, ADME−Toxicology−PKPD, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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Severina HI, Skupa OO, Voloshchuk NI, Saidov N, Bunyatyan VA, Kovalenko SM, Georgiyants VA. Molecular docking, ADMET study and in vivo pharmacological research of N-(3,4-dimetoxyphenyl)-2-{[2-methyl-6-(pyridine-2-yl)-pyrimidin-4-yl]thio}acetamide as a promising anticonvulsant. RESEARCH RESULTS IN PHARMACOLOGY 2020. [DOI: 10.3897/rrpharmacology.6.53332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: The search for new anticonvulsants for epilepsy treatment with higher efficacy and better tolerability remains important. The aim of the present research was an in silico and in vivo pharmacological study of N-(3,4-dimethoxyphenyl)-2-((2-methyl-6-(pyridin-2-yl)pyrimidin-4-yl)thio)acetamide (Epirimil) as a promising anticonvulsant.
Materials and methods: A 1H and 13C NMR spectroscopy, LS/MS, and an elemental analysis were used to determine Epirimil structure. An ADMET analysis, as well as a docking study using anticonvulsant biotargets, e.g.: GABAAR, GABAAT, CA II, NMDAR, and AMPAR, was carried out. Anticonvulsant activity was proved, using PTZ- and MES-induced seizures in rats and mice, and neurotoxicity was determined using a rotarod test. Influence of Epirimil on the psycho-emotional state of the laboratory animals was determined by an open field test.
Results and discussion: A synthesis method of a promising anticonvulsant Epirimil was modified. The calculated ADMET parameters and a molecular docking into the active sites of anticonvulsant biotargets allowed evaluating the research prospects and predicting possible mechanisms for implementing anticonvulsant activity. A prominent anticonvulsant activity of Epirimil was established using in vivo studies on the model of PTZ-induced seizures in rats and MES-induced seizures in mice. In terms of toxicity, Epirimil belongs to class IV – low toxic substances. The open field test showed that Epirimil had almost no effect on the animals’ behavioral responses: it neither changed their psycho-emotional activity, nor increased their anxiety level. ED50, TD50 and protective index of Epirimil according to its anticonvulsant activity were calculated.
Conclusion: The obtained experimental results substantiate the prospects of N-(3,4-Dimethoxyphenyl)-2-((2-methyl-6-(pyridin-2-yl)pyrimidin-4-yl)thio)acetamide as a promising active pharmaceutical ingredient having a multifactor mechanism of anticonvulsant activity.
Graphical abstract
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Macarini AF, Sobrinho TUC, Rizzi GW, Corrêa R. Pyrazole–chalcone derivatives as selective COX-2 inhibitors: design, virtual screening, and in vitro analysis. Med Chem Res 2019. [DOI: 10.1007/s00044-019-02368-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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High-throughput hydrogen bond strength calculation and its applications in optimizing drug ADME properties. Future Med Chem 2019; 11:511-524. [PMID: 30892942 DOI: 10.4155/fmc-2018-0470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
AIM Modifying the molecule's intrinsic hydrogen bond strength (HBS) is a useful approach in optimizing its permeability and P-glycoprotein (P-gp) efflux. Quantum mechanics (QM) based computation has been utilized to estimate the molecular intrinsic HBS. Despite its usefulness, the computation is time consuming for a large set of molecules. METHODOLOGY/RESULTS We introduced a fragment-based high-throughput HBS calculation method and validated it with internal and external datasets. Examples have been presented where the P-gp efflux and permeability can be optimized by modulating calculated HBS. CONCLUSION The results will enable medicinal chemists to calculate HBS in a high-throughput manner while optimizing permeability and P-gp efflux. This will further improve the efficiency of balancing multiple properties during drug discovery process.
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Zhou Y, Cahya S, Combs SA, Nicolaou CA, Wang J, Desai PV, Shen J. Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets. J Chem Inf Model 2019; 59:1005-1016. [PMID: 30586300 DOI: 10.1021/acs.jcim.8b00671] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machine learning models are built to understand quantitative structure-activity relationships (QSARs) and predict molecular activities, including absorption, distribution, metabolism, and excretion (ADME) properties, using only molecular structures. Previous reports have demonstrated the advantages of using deep neural networks (DNNs) for QSAR modeling. One of the challenges while building DNN models is identifying the hyperparameters that lead to better generalization of the models. In this study, we investigated several tunable hyperparameters of deep neural network models on 24 industrial ADME data sets. We analyzed the sensitivity and influence of five different hyperparameters including the learning rate, weight decay for L2 regularization, dropout rate, activation function, and the use of batch normalization. This paper focuses on strategies and practices for DNN model building. Further, the optimized model for each data set was built and compared with the benchmark models used in production. Based on our benchmarking results, we propose several practices for building DNN QSAR models.
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Affiliation(s)
- Yadi Zhou
- Department of Chemistry and Biochemistry , Ohio University , Athens , Ohio 45701 , United States
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Danielson ML, Sawada GA, Raub TJ, Desai PV. In Silico and in Vitro Assessment of OATP1B1 Inhibition in Drug Discovery. Mol Pharm 2018; 15:3060-3068. [DOI: 10.1021/acs.molpharmaceut.8b00168] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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