About the Research
Research Theme
In silico design of new molecules, materials and reactions
Keyword
Chemical databases, Quantitative Structure-Property Relationships (QSPR), Machine learning
Research Outline
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.
Research History
See my CV.
For details on MANABIYA course topics, please follow this link. To learn more about MANABIYA in general, please click here.
Representative Research Achievements
- 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
Related Research
Publications
2023
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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, e202218659,
DOI: 10.1002/anie.202218659
2022
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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
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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