研究紹介
研究テーマ
In silico design of new molecules, materials and reactions
キーワード
Chemical databases, Quantitative Structure-Property Relationships (QSPR), Machine learning
研究概要
My research was focused on exploration and analysis of chemical space, as well as Quantitative Structure-Property Relationships modeling, from redox properties to the biological activity of antimalarial drugs or pairs of anti-cancer drugs. Now, in addition to QSPR modeling of reaction parameters, I study approaches for generation of novel compounds and reactions by Deep neural networks.
経歴
経歴書を参照願います。
MANABIYAコースの研修内容はこちらです。MANABIYA全般について詳しく知りたい方は、こちらをクリックしてください。
代表的な研究成果
- Mappability of drug-like space: towards a polypharmacologically competent map of drug-relevant compounds
P. Sidorov, H. Gaspar, G. Marcou, A. Varnek, D. Horvath, J. Comp.Aid. Mol. Des., 2015, 29, 1087-1108
DOI: 10.1007/s10822-015-9882-z - AntiMalarial Mode of Action (AMMA) Database: Data Selection, Verification and Chemical Space Analysis
P. Sidorov, E. Davioud‐Charvet, G. Marcou, D. Horvath, A. Varnek, Mol. Inf., 2018, 9-10, 1800021
DOI: 10.1002/minf.201800021 - Electrochemical Properties of Substituted 2‐Methyl‐1, 4‐Naphthoquinones: Redox Behavior Predictions
M. Elhabiri, P. Sidorov, E. Cesar‐Rodo, G. Marcou, D.A. Lanfranchi, E. Davioud‐Charvet, D. Horvath, A. Varnek, Chem. Eur. J., 2015, 21, 3415-3424
DOI: 10.1002/chem.201403703 - Predicting synergism of cancer drug combinations using NCI-ALMANAC data
P. Sidorov, S. Naulaerts, J. Ariey-Bonnet, E. Pasquier, P. Ballester, Front. In Chem., 2019, 7, 509
DOI: 10.3389/fchem.2019.00509 - Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data
H. Li, J. Peng, P. Sidorov, Y. Leung, K.-S. Leung, M.-H. Wong, G. Lu, P. Ballester, Bioinformatics, 2019, 35, 3989-3995
DOI: 10.1093/bioinformatics/btz183
関連する研究記事
業績一覧
2023年
-
A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis
, N. Tsuji, Chem. Eur. J., 2023, , e202302837
DOI: 10.1002/chem.202302837
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Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts
, T. Madzhidov, P. Polishchuk, P. Sidorov, A. Varnek, J. Chem. Inf. Model., 2023, 63, 21, 6629–6641
DOI: 10.1021/acs.jcim.3c00393
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Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors
, P. Sidorov, C. D. Zhu, Y. Nagata, T. Gimadiev, A. Varnek, B. List, Angew. Chem., Int. Ed., 2023, ,
DOI: 10.1002/anie.202218659
2022年
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CGRdb2.0: A Python Database Management System for Molecules, Reactions, and Chemical Data
, R. Nugmanov, A. Khakimova, A. Fatykhova, T. Madzhidov, P. Sidorov, A. Varnek, J. Chem. Inf. Model., 2022, 62, 2015-2020
DOI: 10.1021/acs.jcim.1c01105
2021年
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Atom-to-Atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies
, N. Dyubankova, T. I. Madzhidov, R. I. Nugmanov, J. Verhoeven, T. R. Gimadiev, V. A. Afonina, Z. Ibragimova, A. Rakhimbekova, P. Sidorov, A. Gedich, R. Suleymanov, R. Mukhametgaleev, J. Wegner, H. Ceulemans, A. Varnek, Molecular Informatics, 2021, 41,
DOI: 10.1002/minf.202100138
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Reaction Data Curation I: Chemical Structures and Transformations Standardization
, A. Lin, V. A. Afonina, D. Batyrshin, R. I. Nugmanov, T. Akhmetshin, P. Sidorov, N. Duybankova, J. Verhoeven, J. Wegner, H. Ceulemans, A. Gedich, T. I. Madzhidov, A. Varnek, Molecular Informatics, 2021, 40,
DOI: 10.1002/minf.202100119
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Discovery of Novel Chemical Reactions by Deep Generative Recurrent Neural Network
, I. Baskin, T. Gimadiev, A. Mukanov, R. Nugmanov, P. Sidorov, G. Marcou, D. Horvath, O. Klimchuk, T. Madzhidov, A. Varnek, Scientific Reports, 2021, 11, 15
DOI: 10.1038/s41598-021-81889-y
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Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data
, R. Nugmanov, D. Batyrshin, T. Madzhidov, S. Maeda, P. Sidorov, A. Varnek, J. Chem. Inf. Model., 2021, 61, 554-559
DOI: 10.1021/acs.jcim.0c01280