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 researchers 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.