Roman Kyrylenko

ArtiDock is a machine learning-based protein-ligand docking method that improves accuracy over classical techniques while maintaining low computational cost for high-throughput virtual screening.

This study evaluates ML-based conformational sampling in rigid protein-protein docking and finds it does not improve docking accuracy compared to using Apo structures.

This paper improves ADMET prediction by augmenting Mol2Vec embeddings with classical descriptors and feature selection, achieving top results on 10 of 16 TDC benchmarks.