研究紹介
研究テーマ
Computer-aided synthesis planning
キーワード
Chemoinformatics, Machine learning
研究概要
My projects focus on the development of tools for computer-aided retro- and forward-synthesis. These tools combine search algorithms and deep learning methods.
代表的な研究成果
- QSAR modeling based on conformation ensembles using a multi-instance learning approach
D. Zankov, M. Matveieva, A. Nikonenko, R. Nugmanov, I. Baskin, A. Varnek, P. Polishchuk, T. Madzhidov. J. Chem. Inf. Model., 2021, 61(10), 4913–23.
DOI: 10.1021/acs.jcim.1c00692 - Conjugated Quantitative Structure-Property Relationship Models: Application to Simultaneous Prediction of Tautomeric Equilibrium Constants and Acidity of Molecules
D. Zankov, T. Madzhidov, T. Gimadiev, R. Nugmanov, M. Kazymova, I. Baskin, A. Varnek.
J. Chem. Inf. Model., 2021, 61(10), 4913–23.
DOI: 10.1021/acs.jcim.9b00722 - Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity
D. Zankov, P Polishchuk, T. Madzhidov, A. Varnek. Synlett., 2021, 18, 1833-1836.
DOI: 10.1055/a-1553-0427<
業績一覧
2023年
-
Chemical Complexity Challenge: Is Multi-Instance Machine Learning a Solution?
, 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
, T. Madzhidov, P. Polishchuk, P. Sidorov, A. Varnek, J. Chem. Inf. Model., 2023, 63, 21, 6629–6641
DOI: 10.1021/acs.jcim.3c00393