Accelerating Two Algorithms for Large-Scale Compound Selection on GPUs

Abstract

Compound selection procedures based on molecular similarity and diversity are widely used in drug discovery. Current algorithms are often time consuming when applied to very large compound sets. This paper describes the acceleration of two selection algorithms (the leader and the spread algorithms) on graphical processing units (GPUs). We first parallelized the molecular similarity calculation based on Daylight fingerprints and the Tanimoto index and then implemented the two algorithms on GPU hardware using the open source Thrust library. Experiments show that the GPU leader algorithm is 73-120 times faster than the CPU version, and the GPU spread algorithm is 78-143 times faster than the CPU version.