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Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

preprint
submitted on 22.08.2019 and posted on 23.08.2019 by Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli
AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand- receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to improve the docking throughput up to 170-fold. Moreover, we introduce the gradient-based local search method ADADELTA, which is more efficient than the original Solis-Wets method, especially for conformationally complex ligands. We estimate a 1300x reduction in the number of scoring function calls for ligands with 20 rotatable bonds, and even higher reductions likely for more complex ligands. The improvements reported here, both in terms of docking throughput and search efficiency, expand the domain of applicability of the AutoDock4 scoring function considerably.

Funding

AutoDock Suite: Next Generation Environment for Drug Design

National Institute of General Medical Sciences

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ALEPRONA funding program #57186883 from the German Academic Exchange Service (DAAD)

Peruvian National Program for Scholarships and Educational Loans (PRONABEC)

History

Email Address of Submitting Author

forli@scripps.edu

Institution

The Scripps Research Institute

Country

United States

ORCID For Submitting Author

0000-0002-5964-7111

Declaration of Conflict of Interest

None

Exports