Accelerating AUTODOCK4 with GPUs and Gradient-Based Local Search

AUTODOCK4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physicsinspired 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.

Automated summary

AUTODOCK4 is a widely used program for docking small molecules to macromolecular targets, but its long execution times limit its applicability to large scale dockings. To address this issue, an OpenCL implementation called AUTODOCK-GPU has been developed, leveraging GPU hardware to significantly reduce docking runtime by up to 350-fold.