Docking optimization, variance and promiscuity for large-scale drug-like chemical space using high performance computing architectures

Abstract

There is a continuing need to hasten and improve protein-ligand docking to facilitate the next generation of drug discovery. As the drug-like chemical space reaches into the billions of molecules, increasingly powerful computer systems are required to probe, as well as tackle, the software engineering challenges needed to adapt existing docking programs to use next-generation massively parallel processing systems. We demonstrate docking setup using the wrapper code approach to optimize the DOCK program for large-scale computation as well as docking analysis using variance and promiscuity as examples. Wrappers provide faster docking speeds when compared with the naive multi-threading system MPI-DOCK, making future endeavors in large-scale docking more feasible; in addition, eliminating highly variant or promiscuous compounds will make databases more useful.