A geometric deep learning approach to predict binding conformations of bioactive molecules

Understanding the interactions formed between a ligand and its molecular target is key to guiding the optimization of molecules. Different experimental and computational methods have been applied to better understanding these intermolecular interactions. Here we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. The model learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design. Predicting binding of ligands to molecular targets is a key task in the development of new drugs. To improve the speed and accuracy of this prediction, Mendez–Lucio and colleagues developed DeepDock, a method that uses geometric deep learning to inform a statistical potential to find conformations of ligand–target pairs.