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A Deep-Learning Approach Toward Rational Molecular Docking Protocol Selection

submitted on 20.04.2020, 09:31 and posted on 21.04.2020, 12:32 by Jose Jimenez-Luna, Alberto Cuzzolin, Giovanni Bolcato, Mattia Sturlese, Stefano Moro
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 work we have 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 evaluate 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 guesses on which protocol is best suited for their particular protein-ligand pair.


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ETH Zuerich



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Declaration of Conflict of Interest

no conflict of interest