Glide WS: Methodology and Initial Assessment of Performance for Docking Accuracy and Virtual Screening

Powered by dramatic advances in computer hardware, the advent of ultralarge make-on-demand virtual libraries, and a shift in small-molecule discovery toward more challenging targets with limited known actives, there has been a growing interest in the development of performant virtual screening methods that can reliably deliver novel hits. We report on a new method called Glide WS, that builds on our earlier efforts (WScore) to introduce an explicit representation of water structure and dynamics to an empirical scoring function suitable for high-throughput docking. This scoring function has been carefully tuned using absolute binding free energy perturbation calculations (ABFEP). Compared with Glide SP, Glide WS offers significant gains in the two primary tasks for molecular docking in drug discovery, pose prediction and virtual screening enrichment. For docking accuracy, Glide WS achieves a self-docking accuracy of 92% on a diverse set of 1477 protein ligand complexes as compared to 85% for Glide SP, using a criterion of 2.5 Å. We also demonstrate significantly improved virtual screening enrichment using a diverse data set covering of 38 targets together with three different computationally generated libraries of decoys, combined with standard known ChEMBL actives. We focus on ligands ranked in the top few percent of the database (the subset that is relevant to practical virtual screening efforts) and demonstrate that, along with improved enrichment of ChEMBL actives, Glide WS achieves a remarkable reduction in the number of poorly scoring decoys (as calibrated by ABFEP calculations), across a high percentage of targets, as compared to Glide SP. These results suggest that considerably higher hit rates will be observed, as compared to conventional rigid receptor docking, in practical virtual screening applications.