Novel GPU engines for virtual screening of giga-sized libraries identify inhibitors of challenging targets.

To accelerate virtual ligand screening (VLS) and identify potent drug leads from massive chemical libraries, we developed two GPU-accelerated methods: Rapid Docking GPU Engine (RIDGE) for receptor-based screening and Rapid Isostere Discovery Engine (RIDE) for ligand-based 3D similarity screening. RIDGE performance surpassed or was as good as previously described methods when tested on 102 proteins from Directory of Useful Decoys-Enhanced (DUD-E). We used RIDGE and RIDE to screen ultra-large virtual libraries against challenging cancer targets, PD-L1 and K-Ras G12D. This led to the discovery of novel inhibitors with single-digit to sub-micromolar affinities (five for PD-L1, three for K-Ras G12D). Docking scores from our methods were better predictors of binding than conventional VLS. These novel GPU-accelerated methods expand screenable chemical space and successfully identify active lead compounds, even for challenging targets. Further optimization and libraries with higher molecular weight cutoffs could further improve targeting of non-druggable proteins.