GPU-Accelerated Flexible Molecular Docking
Mengran Fan, Jian Wang, Huaipan Jiang, Yilin Feng, Mehrdad Mahdavi, Kamesh Madduri, Mahmut Kandemir, Nikolay V. Dokholyan
Abstract
Virtual screening is a key enabler of computational drug discovery and requires accurate and efficient structure-based molecular docking. In this work, we develop algorithms and software building blocks for molecular docking that can take advantage of graphics processing units (GPUs). Specifically, we focus on MedusaDock, a flexible protein-small molecule docking approach and platform. We accelerate the performance of the coarse docking phase of MedusaDock, as this step constitutes nearly 70% of total running time in typical use-cases. We perform a comprehensive evaluation of the quality and performance with single-GPU and multi-GPU acceleration using a data set of 3875 protein–ligand complexes. The algorithmic ideas, data structure design choices, and performance optimization techniques shed light on GPU acceleration of other structure-based molecular docking software tools.