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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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Pogodin PV, Salina EG, Semenov VV, Raihstat MM, Druzhilovskiy DS, Filimonov DA, Poroikov VV. Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv. SAR QSAR Environ Res 2024; 35:53-69. [PMID: 38282553 DOI: 10.1080/1062936x.2024.2304803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/07/2024] [Indexed: 01/30/2024]
Abstract
Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.
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Affiliation(s)
- P V Pogodin
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - E G Salina
- Group of Biochemistry of Adaptation of Microorganisms, Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russia
| | - V V Semenov
- Laboratory of Medicinal Chemistry (N17), N. D. Zelinsky Institute of Organic Chemistry RAS, Moscow, Russia
| | - M M Raihstat
- Laboratory of Medicinal Chemistry (N17), N. D. Zelinsky Institute of Organic Chemistry RAS, Moscow, Russia
| | - D S Druzhilovskiy
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - D A Filimonov
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - V V Poroikov
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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Sun X, Tamura R, Sumita M, Mori K, Terayama K, Tsuda K. Integrating Incompatible Assay Data Sets with Deep Preference Learning. ACS Med Chem Lett 2022; 13:70-75. [PMID: 35047110 PMCID: PMC8762726 DOI: 10.1021/acsmedchemlett.1c00439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/27/2021] [Indexed: 11/30/2022] Open
Abstract
A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure-activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets. In a task of predicting efficacy of factor Xa inhibitors, DPDI successfully integrated 2959 molecules distributed in 129 assay data sets. In most of our experiments, data integration improved prediction accuracy strongly in interpolation and extrapolation tasks, indicating that DPDI is an effective tool for QSAR studies.
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Affiliation(s)
- Xiaolin Sun
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Ryo Tamura
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Masato Sumita
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Kenichi Mori
- Astellas
Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan
| | - Kei Terayama
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, Yokohama 230-0045, Japan
| | - Koji Tsuda
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
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Pogodin PV, Lagunin AA, Rudik AV, Filimonov DA, Druzhilovskiy DS, Nicklaus MC, Poroikov VV. How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors. Front Chem 2018; 6:133. [PMID: 29755970 PMCID: PMC5935003 DOI: 10.3389/fchem.2018.00133] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 04/09/2018] [Indexed: 12/16/2022] Open
Abstract
Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.
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Affiliation(s)
- Pavel V. Pogodin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Alexey A. Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
- Department of Bioinformatics, Medical-Biological Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anastasia V. Rudik
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Dmitry A. Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | | | - Mark C. Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, NIH, NCI-Frederick, Frederick, MD, United States
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