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 the Directory of Useful Decoys, Enhanced (DUD-E). We used RIDGE and RIDE to screen ultralarge virtual libraries against challenging cancer targets, PD-L1 and K-Ras G12D. This led to the discovery of novel inhibitors with single-digit to submicromolar 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 hits, even for challenging targets. Further optimization and libraries with higher-molecular-weight cutoffs could further improve targeting of nondruggable proteins.