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Postdoctoral Fellow
List Platform (Sidorov Group)

timur.gimadiev atmark

SIDOROV, Pavel Group
Junior PI
Postdoctoral Fellows

About the Research

Research Theme

Generation of novel reactions by means of reccurent neural networks


Chemoinformatics, QSAR, Data mining, Neural networks, Chemical databases, Quantum chemistry, Organic synthesis.
Research Outline

I graduated from the Institute of Chemistry, Department of Organoelement and Polymer Chemistry (Kazan, Russia) in 2009. My diploma work was devoted to the synthesis of organophosphorus compounds and study of their polyhedral complexes with copper. Then, I was employed at the same lab as synthetic chemist. In 2012, I entered to Double-Diploma Master Program in Chemoinformatics between Kazan Federal University and University of Strasbourg. My master thesis was devoted to profiling of biological activities for molecules with Generative Topographic Mapping approach. My PhD project at the University of Strasbourg concerned development of predictive models for kinetic and theromynamic properties of reactions using the Condensed Graph of Reaction approach. Then I worked in Kazan Federal University on reaction data analysis, structure-reactivity modeling and computer-aided synthesis design, and participated in other projects related to in silico drug design and predictive modeling of material properties. Now I am working at ICReDD, Hokkaido University, Japan on generation of novel reactions by means of Recurrent Neural Networks and development of database for fast storage and search of chemical data.

Representative Research Achievements

  • Sydnone-alkyne cycloaddition: Which factors are responsible for reaction rate ?
    T.R. Gimadiev, O. Klimchuk, R.I. Nugmanov, T.I. Madzhidov, A. Varnek , J. Mol. Struct., 2019, 1198
    DOI: 10.1016/j.molstruc.2019.126897
  • CGRtools: Python Library for Molecule, Reaction, and Condensed Graph of Reaction Processing
    R.I. Nugmanov, R.N. Mukhametgaleev, T. Akhmetshin, T.R. Gimadiev, V.A. Afonina, T.I. Madzhidov, A. Varnek , JCIM, 2019, 59, 2516–2521
    DOI: 10.1021/acs.jcim.9b00102
  • Bimolecular Nucleophilic Substitution Reactions: Predictive Models for Rate Constants and Molecular Reaction Pairs Analysis
    T. Gimadiev, T. Madzhidov, I. Tetko, R. Nugmanov, I. Casciuc, O. Klimchuk, A. Bodrov, P. Polishchuk, I. Antipin, A. Varnek , Mol. Inf., 2019, 38
    DOI: 10.1002/minf.201800104
  • Conjugated Quantitative Structure-Property Relationship Models: Application to Simultaneous Prediction of Tautomeric Equilibrium Constants and Acidity of Molecules
    D.V. Zankov, T.I. Madzhidov, A. Rakhimbekova, T.R. Gimadiev, R.I. Nugmanov, M.A. Kazymova, I.I. Baskin, A. Varnek , JCIM., 2019
    DOI: 10.1021/acs.jcim.9b00722
  • Assessment of tautomer distribution using the condensed reaction graph approach
    T.R. Gimadiev, T.I. Madzhidov, R.I. Nugmanov, I.I. Baskin, I.S. Antipin, A. Varnek, JCAMD., 2018, 32, 401–414
    DOI: 10.1007/s10822-018-0101-6
  • Structure–reactivity modeling using mixture-based representation of chemical reactions
    P. Polishchuk, T. Madzhidov, T. Gimadiev, A. Bodrov, R. Nugmanov, A. Varnek, JCAMD., 2017, 31, 829–839
    DOI: 10.1007/s10822-017-0044-3

Related Research



  • Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors
    N. Tsuji, P. Sidorov, C. D. Zhu, Y. Nagata, T. Gimadiev, A. Varnek, B. List, Angew. Chem., Int. Ed., 2023, ,
    DOI: 10.1002/anie.202218659


  • CGRdb2.0: A Python Database Management System for Molecules, Reactions, and Chemical Data
    T. Gimadiev, 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


  • Machine Learning Modelling of Chemical Reaction Characteristics: Yesterday, Today, Tomorrow
    T. I. Madzhidov, A. Rakhimbekova, V. A. Afonina, T. R. Gimadiev, R. N. Mukhametgaleev, R. I. Nugmanov, I. Baskinc, A. Varnekkd, Mendeleev Communications, 2021, 31, 769-780
    DOI: 10.1016/j.mencom.2021.11.003
  • Atom-to-Atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies
    A. Lin, 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
  • Discovery of Novel Chemical Reactions by Deep Generative Recurrent Neural Network
    W. Bort, 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
  • Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data
    T. Gimadiev, 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


  • Conjugated Quantitative Structure-Property Relationship Models: Application to Simultaneous Prediction of Tautomeric Equilibrium Constants and Acidity of Molecules
    DV. Zankov, TI. Madzhidov, A. Rakhimbekova, TR. Gimadiev, RI. Nugmanov, MA. Kazymova, II. Baskin, A. Varnek, J. Chem. Inf. Model., 2019, 59, 4569-4576
    DOI: 10.1021/acs.jcim.9b00722