On the rise of AI technologies in virtual screening

Abstract

AI foundational models for predicting protein-ligand interactions and binding affinities have started to emerge. We challenged Boltz-2 on a difficult dataset constructed on ten ultra-large virtual screening hit lists of pharmacologically relevant targets with in vitro binding assays. We show that Boltz-2 is the best classifier, with a success rate twice that of any other rescoring strategy. Ligand classifications by Boltz-2 are straightforward, accurate, efficient and robust, opening to million-compound accurate rankings on commodity resources.