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Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking

2021; Elsevier BV; Volume: 109; Linguagem: Inglês

10.1016/j.parco.2021.102861

1872-7336

Leonardo Solis-Vasquez, Andreas F. Tillack, Diogo Santos‐Martins, Andreas Koch, Scott LeGrand, Stefano Forli,

Machine Learning in Materials Science

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.