CoSP: Co-supervised pretraining of pocket and ligand

Abstract

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.Preprint. Under review.

Why Deep Models Often Cannot Beat Non-deep Counterparts on Molecular Property Prediction?

“…More recently, some hybrid architectures (Rong et al, 2020;Ying et al, 2021;Min et al, 2022) of GNNs and transformers are emerging to capture the topological structures of molecular graphs. Additionally, given that the available labels for molecules are often expensive or incorrect (Xia et al, 2021;Tan et al, 2021;Xia et al, 2022a), the emerging self-supervised pre-training strategies (You et al, 2020;Xia et al, 2022c;Yue et al, 2022;Liu et al, 2023) on graph-structured data are promising for molecular graph data (Hu et al, 2020;Xia et al, 2023a;Gao et al, 2022), just like the overwhelming success of pre-trained language models in natural language processing community (Devlin et al, 2019;Zheng et al, 2022).…”