Dzvenymyra Yarish, Sofiya Garkot, Oleksandr Grygorenko, Dmytro Radchenko, Yurii Moroz, and Oleksandr Gurbych. “Advancing molecular graphs with descriptors for the prediction of chemical reaction yields”. Journal of Computational Chemistry 43.28 (2022), pp. 1887–1935.
Tymofii Nikolaienko, Oleksandr Gurbych, and Maksym Druchok. “Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network”. Journal of Computational Chemistry 43.10 (2022), pp. 728–739.
Maksym Druchok, Dzvenymyra Yarish, Sofiya Garkot, Tymofii Nikolaenko, and Oleksandr Gurbych. “Ensembling machine learning models to boost molecular affinity prediction”. Computational Biology and Chemistry 93 (2021)
Maksym Druchok, Dzvenymyra Yarish, Oleksandr Gurbych, and Mykola Maksymenko. “ Toward efficient generation, correction, and properties control of unique drug-like structures”. Journal of Computational Chemistry 42.11 (2021), pp. 746–760.
Grygoriy Dolgonos, Alexey Tsukanov, Sergey Psakhie Psakhie, Oleg Lukin, Oleksandr Gurbych, and Alexander Shivanyuk Shivanyuk. “Theoretical studies of capsular complexes of C2V-symmetrical re- sorcin[4]arene tetraesters with tetramethylammonium cation”. Computational and Theoretical Chemistry 1159 (2019), pp. 12–17.
Oleksandr Gurbych and Maksym Prymachenko. “Method for reductive pruning of neural networks and its applications”. Computer Systems and Information Technologies 3 (2022), pp. 40–48.
Олександр Гурбич. “Метод машинного навчання для створення нових лiкарських речовин iз заданими властивостями”. Наук. вiсник Ужгород. ун-ту 40.1 (2022), pp. 126–145.
Олександр Гурбич. “Метод мета-навчання для визначення молекулярної спорiдненостi”. Вiсник Хмельницького нацiонального унiверситету 307.2 (2022), pp. 14–24.
