GNINA 1.0: molecular docking with deep learning

Journal of Cheminformatics

Rishal Aggarwal¹  Rocco Meli²  Matthew Ragoza³  Jocelyn Sunseri³  Paul Francoeur³  Tomohide Masuda³  David Ryan Koes³  Andrew T. McNutt³ 

[1] Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, 500 032, Hyderabad, India;Department of Biochemistry, University of Oxford, Oxford, United Kingdom;Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA;

关键词: Molecular docking;    Deep learning;    Structure-based drug design;   

DOI  :  10.1186/s13321-021-00522-2

来源: Springer

【 摘 要 】

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

【 授权许可】

CC BY