ヴァーネック・アレクサンドル

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ヴァーネック・アレクサンドル
主任研究者, 教授
ストラスブール大学
研究分野
ケモインフォマティクス, 化学における人工知能の活用
関連ウェブサイト
連絡先

varnek atmark unistra.fr

アレクサンドル・ヴァーネック グループ
主任研究者
  • thumbnail image
    ヴァーネック・アレクサンドル
教員
博士研究員

研究紹介

研究テーマ

新しい分子、材料、反応の計算による設計

キーワード

ケモインフォマティクス、定量的構造活性相関、合成デザイン、反応の縮約グラフ、化合物空間解析、ビッグデータ、人工知能

研究概要

我々の研究グループは、ケモインフォマティクスアプローチを用いた新しい分子、材料および反応の、コンピュータによる設計の新規方法論の開発を行っています。特に,構造活性相関のための特性適応型ISIDA記述子、化学反応探索のための縮合グラフによるアプローチ、ビッグデータ解析のための化学反応経路図作成法、およびディープラーニング技術を用いた新規化合物設計とに関わる開発を行っています。

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

  • QSAR modeling: where have you been? Where are you going to?
    A Cherkasov, EN Muratov, D Fourches, A Varnek, II Baskin, M Cronin, J. Med. Chem., 2014, 57, 4977-5010
    DOI : 10.1021/jm4004285
  • Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection
    I. Tetko, I Sushko, A. Pandey, H. Zhu, A Tropsha, E Papa, T Oberg, A. Varnek et al., J. Chem. Inf. Mod., 2008, 48, 1733-1746
    DOI : 10.1021/ci800151m
  • T Oberg, Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis
    H Zhu, A Tropsha, D Fourches, A Varnek, E Papa, P Gramatica, J. Chem. Inf. Mod., 2008, 48, 766-784
    DOI : 10.1021/ci700443v
  • Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures
    A Varnek, D Fourches, F Hoonakker, VP Solov’ev, J. Comput. Aid. Mol. Des., 2005, 19, 693-703
    DOI : 10.1007/s10822-005-9008-0
  • ISIDA-Platform for virtual screening based on fragment and pharmacophoric descriptors
    A Varnek, D Fourches, D Horvath, O Klimchuk, C Gaudin, P Vayer, et al., Current Comput. Aid. Mol. Des., 2008, 4
    DOI : 10.2174/157340908785747465

関連する研究記事

業績一覧

2023年

  • Chemical Complexity Challenge: Is Multi-Instance Machine Learning a Solution?
    D. Zankov, T. Madzhidov, A. Varnek, P. Polishchuk, Wiley Interdisciplinary Reviews-Computational Molecular Science, 2023, ,
    DOI: 10.1002/wcms.1698
  • Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts
    D. Zankov, T. Madzhidov, P. Polishchuk, P. Sidorov, A. Varnek, J. Chem. Inf. Model., 2023, 63, 21, 6629–6641
    DOI: 10.1021/acs.jcim.3c00393
  • Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst Case
    R. Staub, P. Gantzer, Y. Harabuchi, S. Maeda, A. Varnek, Molecules, 2023, 28 (11), 4477
    DOI: 10.3390/molecules28114477
  • 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

2022年

  • 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
  • SynthI: A New Open-Source Tool for Synthon-Based Library Design
    Y. Zabolotna, D. M. Volochnyuk, S. V. Ryabukhin, K. Gavrylenko, D. Horvath, O. Klimchuk, O. Oksiuta, G. Marcou, A. Varnek, J. Chem. Inf. Model., 2022, 62, 2151-2163
    DOI: 10.1021/acs.jcim.1c00754
  • Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach
    V. A. Afonina, D. A. Mazitov, A. Nurmukhametova, M. D. Shevelev, D. A. Khasanova, R. I. Nugmanov, V. A. Burilov, T. I. Madzhidov, A. Varnek, International Journal of Molecular Sciences, 2022, 23,
    DOI: 10.3390/ijms23010248
  • Exploration of the Chemical Space of DNA-Encoded Libraries
    R. Pikalyova, Y. Zabolotna, D. M. Volochnyuk, D. Horvath, G. Marcou, A. Varnek, Molecular Informatics, 2022, 41,
    DOI: 10.1002/minf.202100289

2021年

  • A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry
    Y. Zabolotna, D. M. Volochnyuk, S. V. Ryabukhin, D. Horvath, K. S. Gavrilenko, G. Marcou, Y. S. Moroz, O. Oksiuta, A. Varnek, J. Chem. Inf. Model., 2021, 62, 2171-2185
    DOI: 10.1021/acs.jcim.1c00811
  • 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
  • Reaction Data Curation I: Chemical Structures and Transformations Standardization
    T. R. Gimadiev, 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
  • Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity
    D. Zankov, P. Polishchuk, T. Madzhidov, A. Varnek, Synlett, 2021, 32, 1833-1836
    DOI: 10.1055/a-1553-0427
  • A Critical Overview of Computational Approaches Employed for COVID-19 Drug Discovery
    E. N. Muratov, R. Amaro, C. H. Andrade, N. Brown, S. Ekins, D. Fourches, O. Isayev, D. Kozakov, J. L. Medina-Franco, K. M. Merz, T. I. Oprea, V. Poroikov, G. Schneider, M. H. Todd, A. Varnek, D. A. Winkler, A. V. Zakharov, A. Cherkasov, A. Tropsha, Chemical Society Reviews, 2021, 50, 9121-9151
    DOI: 10.1039/d0cs01065k
  • Computer-Aided Design of New Physical Solvents for Hydrogen Sulfide Absorption
    A. A. Orlov, G. Marcou, D. Horvath, A. E. Cabodevilla, A. Varnek, F. de Meyer, Industrial & Engineering Chemistry Research, 2021, 60, 8588-8596
    DOI: 10.1021/acs.iecr.0c05923
  • NP Navigator: A New Look at the Natural Product Chemical Space
    Y. Zabolotna, P. Ertl, D. Horvath, F. Bonachera, G. Marcou, A. Varnek, Molecular Informatics, 2021, 40,
    DOI: 10.1002/minf.202100068
  • Cross-Validation Strategies in QSPR Modelling of Chemical Reactions
    A. Rakhimbekova, T. N. Akhmetshin, G. I. Minibaeva, R. I. Nugmanov, T. R. Gimadiev, T. I. Madzhidov, I. Baskin, A. Varnek, Sar and Qsar in Environmental Research, 2021, 32, 207-219
    DOI: 10.1080/1062936x.2021.1883107
  • 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

2020年

  • Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions
    A. Rakhimbekova, T. I. Madzhidov, R. I. Nugmanov, T. R. Gimadiev, I. Baskin, A. Varnek, International Journal of Molecular Sciences, 2020, 21, 20
    DOI: 10.3390/ijms21155542
  • QSAR Without Borders (vol 10, Pg 531, 2020)
    E. N. Muratov, J. Bajorath, R. P. Sheridan, I. V. Tetko, D. Filimonov, V. Poroikov, T. I. Oprea, I. Baskin, A. Varnek, A. Roitberg, O. Isayev, S. Curtarolo, D. Fourches, Y. Cohen, A. Aspuru-Guzik, D. A. Winkler, D. Agrafiotis, A. Cherkasov, A. Tropsha, Chemical Society Reviews, 2020, 49, 3716-3716
    DOI: 10.1039/d0cs90041a
  • Application of the Mol2vec Technology to Large-Size Data Visualization and Analysis
    S. Shibayama, G. Marcou, D. Horvath, I. Baskin, K. Funatsu, A. Varnek, Molecular Informatics, 2020, 39, 10
    DOI: 10.1002/minf.201900170

2019年

  • 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