Literature DB >> 33497567

GPU-Accelerated Flexible Molecular Docking.

Mengran Fan1, Jian Wang2, Huaipan Jiang1, Yilin Feng1, Mehrdad Mahdavi1, Kamesh Madduri1, Mahmut T Kandemir1, Nikolay V Dokholyan2,3,4,5.   

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.

Mesh:

Substances:

Year:  2021        PMID: 33497567     DOI: 10.1021/acs.jpcb.0c09051

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  2 in total

1.  Accelerating AutoDock Vina with GPUs.

Authors:  Shidi Tang; Ruiqi Chen; Mengru Lin; Qingde Lin; Yanxiang Zhu; Ji Ding; Haifeng Hu; Ming Ling; Jiansheng Wu
Journal:  Molecules       Date:  2022-05-09       Impact factor: 4.927

2.  NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules.

Authors:  Congzhou M Sha; Jian Wang; Nikolay V Dokholyan
Journal:  Front Mol Biosci       Date:  2022-03-22
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.