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A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection.

ABSTRACT: 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.

SUBMITTER: Jimenez-Luna J

PROVIDER: S-EPMC7321124 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection.

Jiménez-Luna José J Cuzzolin Alberto A Bolcato Giovanni G Sturlese Mattia M Moro Stefano S

Molecules (Basel, Switzerland) 20200527 11

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 mo ...[more]

PMID: 32471211

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