  Can we inject the pocket-ligand interaction knowledge into the pre-trained
model and jointly learn their chemical space? Pretraining molecules and
proteins has attracted considerable attention in recent years, while most of
these approaches focus on learning one of the chemical spaces and lack the
injection of biological knowledge. We propose a co-supervised pretraining
(CoSP) framework to simultaneously learn 3D pocket and ligand representations.
We use a gated geometric message passing layer to model both 3D pockets and
ligands, where each node's chemical features, geometric position and
orientation are considered. To learn biological meaningful embeddings, we
inject the pocket-ligand interaction knowledge into the pretraining model via
contrastive loss. Considering the specificity of molecules, we further propose
a chemical similarity-enhanced negative sampling strategy to improve the
contrastive learning performance. Through extensive experiments, we conclude
that CoSP can achieve competitive results in pocket matching, molecule property
predictions, and virtual screening.
