Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability. - Vadim Korolev; Artem A. Mitrofanov; Alexandru Korotcov; Valery Tkachenko (J. Chem. Inf. Model., 2020)

Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules. - Kirill Karpov; Artem A. Mitrofanov; Vadim Korolev; Valery Tkachenko (CoRR, 2021)

Parametrization of Nonbonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach. - Vadim Korolev; Yuriy M. Nevolin; Thomas A. Manz; Pavel V. Protsenko (J. Chem. Inf. Model., 2021)

Accurate, interpretable predictions of materials properties within transformer language models. - Vadim Korolev; Pavel V. Protsenko (Patterns, 2023)

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design. - Vadim Korolev; Artem A. Mitrofanov (J. Chem. Inf. Model., 2024)