A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection.
chemoinformatics
deep learning
molecular docking
structural biology
Journal
Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009
Informations de publication
Date de publication:
27 May 2020
27 May 2020
Historique:
received:
28
04
2020
revised:
23
05
2020
accepted:
26
05
2020
entrez:
31
5
2020
pubmed:
31
5
2020
medline:
2
3
2021
Statut:
epublish
Résumé
While a plethora of different protein-ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein-ligand pair. In this study, we developed a machine-learning model that uses a combination of convolutional and fully connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluated the performance of our model using a widely available database of protein-ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed selections on which protocol is best suited for their particular protein-ligand pair.
Identifiants
pubmed: 32471211
pii: molecules25112487
doi: 10.3390/molecules25112487
pmc: PMC7321124
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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