NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules

Frontiers in Molecular Biosciences

Jian Wang³  Congzhou M. Sha³  Nikolay V. Dokholyan⁴ 

[1] Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, United States;Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, United States;Department of Pharmacology, Penn State College of Medicine, Hershey, PA, United States;Departments of Chemistry and Biomedical Engineering, Penn State University, University Park, PA, United States;

关键词: virtual docking;    small molecule screening;    drug screening;    machine learning;    binding affinity;   

DOI  :  10.3389/fmolb.2022.867241

来源: DOAJ

【 摘 要 】

Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock, which accelerates the process of high-quality computational docking by a factor of 106, and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy, and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 h, which we validate with physical docking using MedusaDock. Due to its speed and accuracy, NeuralDock may be useful in brute-force virtual screening of massive chemical libraries and training of generative drug models.

【 授权许可】

Unknown