Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.
Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.
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DOI：
10.26434/chemrxiv.9702389
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发表时间：
2019-08
影响因子：
5.5
通讯作者：
Diogo Santos-Martins;Leonardo Solis-Vasquez;A. F. Tillack;M. Sanner;Andreas Koch;Stefano Forli
中科院分区：
文献类型：
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作者：
Diogo Santos-Martins;Leonardo Solis-Vasquez;A. F. Tillack;M. Sanner;Andreas Koch;Stefano Forli
AutoDock4 是一种广泛使用的程序，用于将小分子与大分子靶标对接。它使用物理启发的评分函数描述配体-受体相互作用，该函数已被证明在各种药物发现项目中有用。然而，与更现代和最新的软件相比，AutoDock4 的执行时间更长，限制了其大规模对接的适用性。为了解决这个问题，我们描述了 AutoDock4 的 OpenCL 实现，称为 AutoDock-GPU，它利用 GPU 硬件的高度并行架构，将对接运行时间比单线程进程减少多达 350 倍。此外，我们还引入了基于梯度的局部搜索方法 ADADELTA，以及 AutoDock4 的 Solis-Wets 随机优化器的改进版本。这些高效的本地搜索算法显着减少了产生良好结果所需的评分函数的调用次数。这里报告的改进，无论是在对接吞吐量还是搜索效率方面，都有助于在大规模虚拟筛选中使用 AutoDock4 评分功能。
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.