The combination of deep learning and computer-aided, structure-based drug design is an area of great promise, although recent work has put forth that issues such as dataset bias can negatively impact model development. We present a simple, modular graph-based convolutional neural network that takes the output of a docking procedure as input to generate models for activity and binding mode prediction. We show, in agreement with recent literature, that dataset bias drives many of the promising results on virtual screening that have previously been reported. However, we also show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased dataset is constructed. We develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures that outperforms the baseline docking program in a variety of tests, including on cross-docking datasets that mimic real-world docking use cases.

Challenges and progress in combining docking programs with deep neural networks

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.