Enhancing Metaheuristic-based Virtual Screening Methods on Massively Parallel and Heterogeneous Systems

Abstract

Molecular docking through Virtual Screening is an optimization problem which can be approached with metaheuristic methods. The interaction between two chemical compounds (typically a protein or receptor and small molecule or ligand) is measured with computationally very demanding scoring functions and can, moreover, be measured at several spots throughout the receptor. For the simulation of large molecules, it is necessary to scale to large clusters to deal with memory and computational requirements. In this paper, we analyze the current landscape of computation, where massive parallelism and heterogeneity are today the main ingredients in large-scale computing systems, to enhance metaheuristic-based virtual screening methods, and thus facilitate the analysis of large molecules. We provide a parallelization strategy aimed at leveraging these features. Our solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions under different molecular interactions. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of Virtual Screening methods on high-performance contemporary heterogeneous platforms.