| Ensembling machine learning models to boost molecular affinity prediction M Druchok, D Yarish, S Garkot, T Nikolaienko, O Gurbych Computational Biology and Chemistry 93, 107529, 2021 | 16 | 2021 |
| Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network T Nikolaienko, O Gurbych, M Druchok Journal of Computational Chemistry 43 (10), 728-739, 2022 | 9 | 2022 |
| Toward efficient generation, correction, and properties control of unique drug‐like structures M Druchok, D Yarish, O Gurbych, M Maksymenko Journal of Computational Chemistry 42 (11), 746-760, 2021 | 9 | 2021 |
| Advancing molecular graphs with descriptors for the prediction of chemical reaction yields D Yarish, S Garkot, OO Grygorenko, DS Radchenko, YS Moroz, ... Journal of Computational Chemistry 44 (2), 76-92, 2023 | 7 | 2023 |
| Theoretical studies of capsular complexes of C2V-symmetrical resorcin [4] arene tetraesters with tetramethylammonium cation G Dolgonos, A Tsukanov, SG Psakhie, O Lukin, O Gurbych, A Shivanyuk Computational and Theoretical Chemistry 1159, 12-17, 2019 | 4 | 2019 |
| High throughput screening with machine learning O Gurbych, M Druchok, D Yarish, S Garkot arXiv preprint arXiv:2012.08275, 2020 | 3 | 2020 |
| Reductive pruning of neural networks and its applications O Gurbych, M Prymachenko Computer systems and information technologies, 40-48, 2022 | 1* | 2022 |
| Machine learning method for novel drug-like substances generation O Gurbych Mathematics and Informatics 40 (1), 126-145, 2022 | 1* | 2022 |
| Meta-learning for molecular affinity prediction O Gurbych VKNU 307 (2), 14-24, 2022 | 1* | 2022 |
| Towards Efficient Generation, Correction and Properties Control of Unique Drug-like Structures M Druchok, Y Dzvenymyra, O Gurbych, M Maksymenko https://chemrxiv.org/engage/chemrxiv/article-details/60c744fbee301c5e2ac79217, 2019 | | 2019 |
