A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection

José Jiménez-Luna; Alberto Cuzzolin; Giovanni Bolcato; Mattia Sturlese; Stefano Moro

A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection

2020

José Jiménez-Luna | Alberto Cuzzolin | Giovanni Bolcato | Mattia Sturlese | Stefano Moro

While a plethora of different protein&ndash:ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein&ndash: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&ndash: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&ndash:ligand pair.

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