SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

High-Throughput Virtual Laboratory for Drug Discovery Using Massive Datasets

Authors: Jens Glaser, Josh V. Vermaas, and David M. Rogers (Oak Ridge National Laboratory); Jeff Larkin and Scott LeGrand (Nvidia Corporation); Swen Boehm and Matthew B. Baker (Oak Ridge National Laboratory); Aaron Scheinberg (Jubilee Development); Andreas F. Tillack (Scripps Research); and Mathialakan Thavappiragasam, Ada Sedova, and Oscar Hernandez (Oak Ridge National Laboratory)

Abstract: Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350x mean speedup over the CPU version (50x speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude.

The resulting 58x speedup for the full pipeline reduces an initial 51 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.

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