ArtiDock: Accurate Machine Learning Approach to Protein-Ligand Docking Optimized for High-Throughput Virtual Screening.

ABSTRACT: Classical protein-ligand docking has been a cornerstone technique in computational drug discovery for decades but has reached an accuracy and performance plateau. Recently introduced Machine Learning (ML)-based docking methods offer a promising paradigm shift, but their practical adoption is hampered by accuracy-to-speed trade-offs, inadequate benchmarking standards, and questionable chemical validity of predicted poses. In this study, we introduce ArtiDock─an ML-based docking technique optimized for high-throughput virtual screening applications. To evaluate ArtiDock, we developed a dedicated performance and accuracy benchmark for pocket-specific rigid protein-ligand docking, which mimics realistic industrial drug discovery scenarios and is based on the novel PLINDER data set. We demonstr

SUBMITTER: Voitsitskyi T 

PROVIDER: S-EPMC12933704 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature