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Yi J, Shi S, Fu L, Yang Z, Nie P, Lu A, Wu C, Deng Y, Hsieh C, Zeng X, Hou T, Cao D. OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds. Nat Protoc 2024; 19:1105-1121. [PMID: 38263521 DOI: 10.1038/s41596-023-00942-4] [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: 11/16/2022] [Accepted: 10/27/2023] [Indexed: 01/25/2024]
Abstract
Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.
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Affiliation(s)
- Jiacai Yi
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Shaohua Shi
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
| | - Ziyi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
| | - Pengfei Nie
- National Supercomputer Center in Tianjin, Tianjin, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
- Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, China
| | - Chengkun Wu
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
| | - Changyu Hsieh
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiangxiang Zeng
- Deparment of Computer Science, Hunan University, Changsha, China
| | - Tingjun Hou
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, China.
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China.
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Jiang H, Li Y, Wang Z, Li S, Wu T, Xiong F. 3D-QSAR, molecular docking, and molecular dynamics analysis of novel biphenyl-substituted pyridone derivatives as potent HIV-1 NNRTIs. J Biomol Struct Dyn 2023:1-16. [PMID: 37909494 DOI: 10.1080/07391102.2023.2276885] [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: 06/26/2023] [Accepted: 10/14/2023] [Indexed: 11/03/2023]
Abstract
When designing new medications targeting HIV-1, drug designers concentrate on reverse transcriptase (RT), the central enzyme of their concern. This is due to its vital role in converting single-stranded RNA into double-stranded DNA throughout the life cycle of HIV-1. In recent reports, a series of newly discovered pyridone derivatives with biphenyl substitutions have emerged as highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs), displaying impressive antiviral activity. To analyse the three-dimensional quantitative structure-activity relationship (3D-QSAR) of pyridone inhibitors with biphenyl substitutions, we employed CoMFA and CoMSIA methods in this study. The dataset comprises a total of 51 compounds. The findings of this research demonstrate that both the CoMFA (q2=0.688, r2=0.976, rpred2=0.831) and CoMSIA/SHE (q2=0.758, r2=0.968, rpred2=0.828) models exhibit excellent predictive capability and reliable estimation stability. According to the findings of the model, we designed a collection of eleven molecules that exhibit the potential for significantly improved predictive activity. We proceeded to investigate the binding patterns of these compounds to receptor proteins utilizing the molecular docking technique. To ensure the reliability of the docking results, we went on to validate them by conducting molecular dynamics simulations and performing accurate calculations of the binding free energy. Moreover, based on initial ADMET predictions, the results consistently indicate that the newly created molecule possesses favourable pharmacokinetic properties. This study will help to facilitate the development of efficient novel inhibitors that specifically target HIV-1's non-nucleoside reverse transcriptase (NNRTIs).Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Huifang Jiang
- Department of Chemistry, University of Shanghai for Science and Technology, Shanghai, P.R. China
| | - Yeji Li
- Department of Chemistry, University of Shanghai for Science and Technology, Shanghai, P.R. China
| | - Zhonghua Wang
- School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai, P.R. China
| | - Shaotong Li
- Department of Chemistry, University of Shanghai for Science and Technology, Shanghai, P.R. China
| | - Tianle Wu
- Department of Chemistry, University of Shanghai for Science and Technology, Shanghai, P.R. China
| | - Fei Xiong
- Department of Chemistry, University of Shanghai for Science and Technology, Shanghai, P.R. China
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Fang C, Wang Y, Grater R, Kapadnis S, Black C, Trapa P, Sciabola S. Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective. J Chem Inf Model 2023. [PMID: 37216672 DOI: 10.1021/acs.jcim.3c00160] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Absorption, distribution, metabolism, and excretion (ADME), which collectively define the concentration profile of a drug at the site of action, are of critical importance to the success of a drug candidate. Recent advances in machine learning algorithms and the availability of larger proprietary as well as public ADME data sets have generated renewed interest within the academic and pharmaceutical science communities in predicting pharmacokinetic and physicochemical endpoints in early drug discovery. In this study, we collected 120 internal prospective data sets over 20 months across six ADME in vitro endpoints: human and rat liver microsomal stability, MDR1-MDCK efflux ratio, solubility, and human and rat plasma protein binding. A variety of machine learning algorithms in combination with different molecular representations were evaluated. Our results suggest that gradient boosting decision tree and deep learning models consistently outperformed random forest over time. We also observed better performance when models were retrained on a fixed schedule, and the more frequent retraining generally resulted in increased accuracy, while hyperparameters tuning only improved the prospective predictions marginally.
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Affiliation(s)
- Cheng Fang
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Ye Wang
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Richard Grater
- DMPK, Biogen, Cambridge, Massachusetts 02142, United States
| | | | - Cheryl Black
- DMPK, Biogen, Cambridge, Massachusetts 02142, United States
| | - Patrick Trapa
- DMPK, Biogen, Cambridge, Massachusetts 02142, United States
| | - Simone Sciabola
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
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Hoover AJ, Spale M, Lahue B, Bitton DA. Matcher: An Open-Source Application for Translating Large Structure/Property Data Sets into Insights for Drug Design. J Chem Inf Model 2023; 63:1852-1857. [PMID: 36977316 DOI: 10.1021/acs.jcim.3c00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
To solve recurring problems in drug discovery, matched molecular pair (MMP) analysis is used to understand relationships between chemical structure and function. For the MMP analysis of large data sets (>10,000 compounds), available tools lack flexible search and visualization functionality and require computational expertise. Here, we present Matcher, an open-source application for MMP analysis, with novel search algorithms and fully automated querying-to-visualization that requires no programming expertise. Matcher enables unprecedented control over the search and clustering of MMP transformations based on both variable fragment and constant environment structure, which is critical for disentangling relevant and irrelevant data to a given problem. Users can exert such control through a built-in chemical sketcher and with a few mouse clicks can navigate between resulting MMP transformations, statistics, property distribution graphs, and structures with raw experimental data, for confident and accelerated decision making. Matcher can be used with any collection of structure/property data; here, we demonstrate usage with a public ChEMBL data set of about 20,000 small molecules with CYP3A4 and/or hERG inhibition data. Users can reproduce all examples demonstrated herein via unique links within Matcher's interface-a functionality that anyone can use to preserve and share their own analyses. Matcher and all its dependencies are open-source, can be used for free, and are available with containerized deployment from code at https://github.com/Merck/Matcher. Matcher makes large structure/property data sets more transparent than ever before and accelerates the data-driven solution of common problems in drug discovery.
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Affiliation(s)
- Andrew J Hoover
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Martin Spale
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Brian Lahue
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Danny A Bitton
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
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Abstract
Matched Molecular Pair Analysis (MMP) is a very important tool during the lead optimization stage in drug discovery. The usefulness of this tool in the lead optimization stage has been discussed in several peer-reviewed articles. The application of MMP in Molecule generation is relatively new. This brings several challenges one of them being the need to encode contextual information into the transforms. In this chapter, we discuss how we use MMPs as a molecule generation method and how does it compare with other molecular generators.
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Affiliation(s)
- Sandeep Pal
- GlaxoSmithKline Medicines Research Centre, Stevenage, UK.
| | - Peter Pogány
- GlaxoSmithKline Medicines Research Centre, Stevenage, UK
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Tuo Y, Li G, Liu Z, Yu N, Li Y, Yang L, Liu H, Wang Y. Discovery of novel antifungal resorcylate aminopyrazole Hsp90 inhibitors based on structural optimization by molecular simulations. NEW J CHEM 2022. [DOI: 10.1039/d1nj04927e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Novel antifungal resorcylate aminopyrazole Hsp90 inhibitors were discovered by 3D-QSAR, molecular docking and molecular dynamics simulations.
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Affiliation(s)
- Yan Tuo
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Guangping Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Zhou Liu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Na Yu
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Yuepeng Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Li Yang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
| | - Haibin Liu
- National Engineering Research Center for Gelatin-based Traditional Chinese Medicine, Dong-E-E-Jiao Co. Ltd., Shandong Province, 252201, China
| | - Yuanqiang Wang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
- Chongqing Key Laboratory of Medicinal Chemistry & Molecular Pharmacology, Chongqing University of Technology, Chongqing, 400054, China
- Chongqing Key Laboratory of Target Based Drug Screening and Activity Evaluation, Chongqing University of Technology, Chongqing, 400054, China
- State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing, 400716, China
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Yang ZY, Fu L, Lu AP, Liu S, Hou TJ, Cao DS. Semi-automated workflow for molecular pair analysis and QSAR-assisted transformation space expansion. J Cheminform 2021; 13:86. [PMID: 34774096 PMCID: PMC8590336 DOI: 10.1186/s13321-021-00564-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/30/2021] [Indexed: 12/01/2022] Open
Abstract
In the process of drug discovery, the optimization of lead compounds has always been a challenge faced by pharmaceutical chemists. Matched molecular pair analysis (MMPA), a promising tool to efficiently extract and summarize the relationship between structural transformation and property change, is suitable for local structural optimization tasks. Especially, the integration of MMPA with QSAR modeling can further strengthen the utility of MMPA in molecular optimization navigation. In this study, a new semi-automated procedure based on KNIME was developed to support MMPA on both large- and small-scale datasets, including molecular preparation, QSAR model construction, applicability domain evaluation, and MMP calculation and application. Two examples covering regression and classification tasks were provided to gain a better understanding of the importance of MMPA, which has also shown the reliability and utility of this MMPA-by-QSAR pipeline. ![]()
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China
| | - Li Fu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, SAR, People's Republic of China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Ting-Jun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China. .,Hunan Key Laboratory of Diagnostic and Therapeutic Drug Research for Chronic Diseases, Changsha, 410013, Hunan, China. .,Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, 999077, SAR, People's Republic of China.
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