| Blind prediction of homo‐and hetero‐protein complexes: The CASP13‐CAPRI experiment MF Lensink, G Brysbaert, N Nadzirin, S Velankar, RAG Chaleil, T Gerguri, ... Proteins: Structure, Function, and Bioinformatics 87 (12), 1200-1221, 2019 | 127 | 2019 |
| Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization M Kadukova, S Grudinin Journal of computer-aided molecular design 31, 943-958, 2017 | 45 | 2017 |
| VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures I Igashov, K Olechnovič, M Kadukova, Č Venclovas, S Grudinin Bioinformatics 37 (16), 2332-2339, 2021 | 37 | 2021 |
| KORP-PL: a coarse-grained knowledge-based scoring function for protein–ligand interactions M Kadukova, KS Machado, P Chacón, S Grudinin Bioinformatics 37 (7), 943-950, 2021 | 29 | 2021 |
| Knodle: a support vector machines-based automatic perception of organic molecules from 3D coordinates M Kadukova, S Grudinin Journal of Chemical Information and Modeling 56 (8), 1410-1419, 2016 | 23 | 2016 |
| Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2 M Kadukova, S Grudinin Journal of computer-aided molecular design 32, 151-162, 2018 | 22 | 2018 |
| Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation S Grudinin, M Kadukova, A Eisenbarth, S Marillet, F Cazals Journal of computer-aided molecular design 30 (9), 791-804, 2016 | 21 | 2016 |
| Hydroxylation of antitubercular drug candidate, SQ109, by mycobacterial cytochrome P450 S Bukhdruker, T Varaksa, I Grabovec, E Marin, P Shabunya, M Kadukova, ... International journal of molecular sciences 21 (20), 7683, 2020 | 18 | 2020 |
| Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4 M Kadukova, V Chupin, S Grudinin Journal of Computer-Aided Molecular Design 34, 191-200, 2020 | 13 | 2020 |
| Regression-based active learning for accessible acceleration of ultra-large library docking E Marin, M Kovaleva, M Kadukova, K Mustafin, P Khorn, A Rogachev, ... Journal of chemical information and modeling 64 (7), 2612-2623, 2023 | 10 | 2023 |
| Chauvot de Beauchêne I MF Lensink, G Brysbaert, N Nadzirin, S Velankar, RAG Chaleil, T Gerguri, ... Maigret, B, 1200-1221, 2019 | 8 | 2019 |
| Convex-PLR – Revisiting affinity predictions and virtual screening using physics-informed machine learning M Kadukova, V Chupin, S Grudinin bioRxiv, 2021.09. 13.460049, 2021 | 1 | 2021 |
| Mapping the space of protein binding sites with sequence-based protein language models T Oruc, M Kadukova, TG Davies, M Verdonk, C Poelking bioRxiv, 2024.07. 24.604735, 2024 | | 2024 |
| Développement de nouveaux modèles, basés sur les données, pour prédire les interactions entre protéines et petites molécules M Kadukova Université Grenoble Alpes, 2021 | | 2021 |
| Development of novel algorithms for data-driven prediction of interactions between proteins and small molecules M Kadukova Université Grenoble Alpes [2020-....]; Moscow Institute of Physics and …, 2021 | | 2021 |
| KORP-PL: a coarse-grained knowledge-based scoring function for protein-ligand interactions P Chacón, M Kadukova, K Santos Machado, S Grudinin Oxford University Press, 2020 | | 2020 |
| Knowledge-based prediction of protein-ligand binding affinities M Kadukova, V Chupin, S Grudinin JOURNAL OF BIOENERGETICS AND BIOMEMBRANES 50 (6), 546-547, 2018 | | 2018 |
| Using machine learning to predict protein-ligand interactions M Kadukova, S Grudinin Understanding Protein Interactions: from Molecules to Organisms, 16, 2017 | | 2017 |
| KNODLE-A MACHINE LEARNING-BASED TOOL FOR PERCEPTION OF ORGANIC MOLECULES FROM 3D COORDINATES MN Kadukova, S Grudinin ХХ Менделеевский съезд по общей и прикладной химии, 138-138, 2016 | | 2016 |
| Supplementary Information for Convex-PL R–Revisiting affinity predictions and virtual screening using physics-informed machine learning. M Kadukova, V Chupin, S Grudinin | | |
