Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

By Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas F. Tillack, Michel F. Sanner, Andreas Koch, Stefano Forli. Journal of Chemical Theory and Computation, 2019

324 citations recorded by the source corpus.

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 reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f20cfb4c508ef252132cfd59a9e61486a64db6c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Stefano Forli
2. David S. Goodsell
3. Matthew Holcomb
4. Ya-Ting Chang
5. Zhifeng Gao
6. Hang Zheng
7. Guolin Ke

In Molecular Biology

1. Stefano Forli
2. Zhifeng Gao
3. Hang Zheng
4. David S. Goodsell
5. Guolin Ke

Citations in the corpus

• Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023, methodology): “Traditional Molecular Docking Approaches AutoDock and its variants [1, 17, 18, 19] are widely used in molecular docking.”
• Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies (2022, background): “In this study, we predicted the protein structure of WSB1 by Alphafold2 and then used structure-based virtual screening, including AutoDock-GPU and Glide, to select compounds.”
• Evaluation of AlphaFold2 structures as docking targets (2022, background): “5.3 (Santos-Martins et al., 2021) with default parameters.”