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CITATION.cff
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CITATION.cff
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cff-version: 1.2.0
title: 'GNINA 1.0: Molecular Docking with Deep Learning'
message: 'If you find gnina useful, please cite our paper(s)'
type: software
authors:
- given-names: Andrew
family-names: McNutt
affiliation: University of Pittsburgh
email: anm329@pitt.edu
- given-names: Paul
family-names: Francoeur
affiliation: University of Pittsburgh
orcid: 'https://orcid.org/0000-0002-1440-567X'
email: paf46@pitt.edu
- given-names: Rishal
family-names: Aggarwal
affiliation: International Institute of Information Technology
- given-names: Tomohide
family-names: Masuda
affiliation: University of Pittsburgh
- given-names: Rocco
family-names: Meli
affiliation: University of Oxford
- given-names: Matthew
family-names: Ragoza
affiliation: University of Pittsburgh
- given-names: Jocelyn
family-names: Sunseri
affiliation: University of Pittsburgh
- given-names: David
family-names: Koes
email: dkoes@pitt.edu
affiliation: University of Pittsburgh
orcid: 'https://orcid.org/0000-0002-6892-6614'
identifiers:
- type: doi
value: 10.26434/chemrxiv.13578140.v1
repository-code: 'https://github.com/gnina/gnina'
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 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
keywords:
- Molecular docking
- Deep learning
- Structure-based drug design
license: GPL-1.0
preferred-citation:
type: article
title: "GNINA 1.0: molecular docking with deep learning"
authors:
- given-names: Andrew
family-names: McNutt
affiliation: University of Pittsburgh
email: anm329@pitt.edu
- given-names: Paul
family-names: Francoeur
affiliation: University of Pittsburgh
orcid: 'https://orcid.org/0000-0002-1440-567X'
email: paf46@pitt.edu
- given-names: Rishal
family-names: Aggarwal
affiliation: International Institute of Information Technology
- given-names: Tomohide
family-names: Masuda
affiliation: University of Pittsburgh
- given-names: Rocco
family-names: Meli
affiliation: University of Oxford
- given-names: Matthew
family-names: Ragoza
affiliation: University of Pittsburgh
- given-names: Jocelyn
family-names: Sunseri
affiliation: University of Pittsburgh
- given-names: David
family-names: Koes
email: dkoes@pitt.edu
affiliation: University of Pittsburgh
orcid: 'https://orcid.org/0000-0002-6892-6614'
journal: "Journal of cheminformatics"
volume: 13
number: 1
start: 1
end: 20
year: 2021
publisher: "BioMed Central"