GNINA 1.0: molecular docking with deep learning.

Journal of Cheminformatics
Andrew McNuttDavid Ryan Koes

Abstract

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 m...Continue Reading

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Citations

Aug 16, 2021·Journal of Cheminformatics·Rocco MeliPhilip C Biggin
Sep 2, 2021·Journal of Chemical Information and Modeling·Fergus BoylesGarrett M Morris
Oct 12, 2021·Journal of Chemical Information and Modeling·Ziqiao XuAaron T Frank
Nov 19, 2021·Journal of Chemical Information and Modeling·Sowmya Ramaswamy KrishnanArijit Roy
Dec 2, 2021·Briefings in Bioinformatics·Yeji WangYong Huang

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Software Mentioned

Medusa
Open Babel
ProDy
DeepVS
PDBbind
TopN
Pymol
AtomNet
BT
ESPH T - Bind

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