  The binding between proteins and ligands plays a crucial role in the realm of
drug discovery. Previous deep learning approaches have shown promising results
over traditional computationally intensive methods, but resulting in poor
generalization due to limited supervised data. In this paper, we propose to
learn protein-ligand binding representation in a self-supervised learning
manner. Different from existing pre-training approaches which treat proteins
and ligands individually, we emphasize to discern the intricate binding
patterns from fine-grained interactions. Specifically, this self-supervised
learning problem is formulated as a prediction of the conclusive binding
complex structure given a pocket and ligand with a Transformer based
interaction module, which naturally emulates the binding process. To ensure the
representation of rich binding information, we introduce two pre-training
tasks, i.e.~atomic pairwise distance map prediction and mask ligand
reconstruction, which comprehensively model the fine-grained interactions from
both structure and feature space. Extensive experiments have demonstrated the
superiority of our method across various binding tasks, including
protein-ligand affinity prediction, virtual screening and protein-ligand
docking.
