期刊
MOLECULES
卷 25, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/molecules25112487
关键词
deep learning; structural biology; chemoinformatics; molecular docking
资金
- RETHINK initiative at ETH Zuerich
- Boehringer Ingelheim Pharma
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.
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