On the Difficulty to Rescore Hits from Ultralarge Docking Screens

Docking-based virtual screening tools customized to mine ultralarge chemical spaces are consistently reported to yield both higher hit rates and more potent ligands than that achieved by conventional docking of smaller million-sized compound libraries. This remarkable achievement is however counterbalanced by the absolute necessity to design an efficient postprocessing of the millions of potential virtual hits for selecting a few chemically diverse compounds for synthesis and biological evaluation. We here retrospectively analyzed ten successful ultralarge virtual screening hit lists that underwent <i>in vitro</i> binding assays, for binding affinity prediction using eight rescoring methods including simple empirical scoring functions, machine learning, molecular-mechanics and quantum-mechanics approaches. Although the best predictions usually rely on the most sophisticated methods, none of the tested rescoring methods could robustly distinguish known binders from inactive compounds, across all assays. Energy refinement of protein-ligand complexes, prior to rescoring, marginally helped molecular mechanics and quantum mechanics approaches but deteriorates predictions from empirical and machine learning scoring functions.