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.