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Article

A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection

1
Department of Chemistry and Applied Biosciences, RETHINK, ETH Zuerich, Vladimir-Prelog-Weg 4, 8093 Zuerich, Switzerland
2
Institute for Pure & Applied Mathematics, University California Los Angeles, 460 Portola Plaza, Los Angeles, CA 90095-7121, USA
3
Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Molecules 2020, 25(11), 2487; https://doi.org/10.3390/molecules25112487
Received: 28 April 2020 / Revised: 23 May 2020 / Accepted: 26 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Drug Discovery and Molecular Docking)
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. View Full-Text
Keywords: deep learning; structural biology; chemoinformatics; molecular docking deep learning; structural biology; chemoinformatics; molecular docking
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MDPI and ACS Style

Jiménez-Luna, J.; Cuzzolin, A.; Bolcato, G.; Sturlese, M.; Moro, S. A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules 2020, 25, 2487. https://doi.org/10.3390/molecules25112487

AMA Style

Jiménez-Luna J, Cuzzolin A, Bolcato G, Sturlese M, Moro S. A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection. Molecules. 2020; 25(11):2487. https://doi.org/10.3390/molecules25112487

Chicago/Turabian Style

Jiménez-Luna, José, Alberto Cuzzolin, Giovanni Bolcato, Mattia Sturlese, and Stefano Moro. 2020. "A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection" Molecules 25, no. 11: 2487. https://doi.org/10.3390/molecules25112487

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