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.

了解配体与其分子靶标之间形成的相互作用是指导分子优化的关键。已应用不同的实验和计算方法来更好地理解这些分子间相互作用。在这里，我们报告了一种基于几何深度学习的方法，该方法能够预测配体与蛋白质靶标的结合构象。该模型基于距离似然学习统计潜力，这是为每个配体-目标对量身定制的。这种潜力可以与全局优化算法相结合，以重现配体的实验结合构象。我们表明，此处描述的基于距离似然的潜力与用于对接和筛选任务的成熟评分函数相似或更好。全面的，