Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores

In drug discovery, molecular docking aims at characterizing the binding of a drug-like molecule to a macromolecule. AutoDock-GPU, a state-of-the-art docking software, estimates the geometrical conformation of a docked ligand-protein complex by minimizing a scoring function. Our profiling results ind...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Schieffer, Gabin, Peng, Ivy
Format: Article
Language:English
Subjects:
Molecular docking
Tensors
Online Access:Get full text
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Summary:In drug discovery, molecular docking aims at characterizing the binding of a drug-like molecule to a macromolecule. AutoDock-GPU, a state-of-the-art docking software, estimates the geometrical conformation of a docked ligand-protein complex by minimizing a scoring function. Our profiling results indicate that the current reduction operation that is heavily used in the scoring function is sub-optimal. Thus, we developed a method to accelerate the sum reduction of four-element vectors using matrix operations on NVIDIA Tensor Cores. We integrated the new reduction operation into AutoDock-GPU and evaluated it on multiple chemical complexes on three GPUs. Our results show that our method for reduction operation is 4-7 times faster than the AutoDock-GPU baseline. We also evaluated the impact of our method on the overall simulation time in the real-world docking simulation and achieved a 27% improvement on the average docking time.
ISSN:2331-8422