ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
2 files

GNINA 1.0: Molecular Docking with Deep Learning

preprint
submitted on 15.01.2021, 00:22 and posted on 18.01.2021, 07:32 by Andrew McNutt, Paul Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, David Koes
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 2A 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 and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

Funding

Methods, Tools and Resources for Interactive Online Virtual Screening and Lead Optimization

National Institute of General Medical Sciences

Find out more...

Combining Machine Learning with Molecular Dynamics to improve rapid protein-ligand predictions

Biotechnology and Biological Sciences Research Council

Find out more...

History

Email Address of Submitting Author

dkoes@pitt.edu

Institution

University of Pittsburgh

Country

United States

ORCID For Submitting Author

0000-0002-6892-6614

Declaration of Conflict of Interest

no conflict of interest

Exports