Original title: Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction
Authors: Jiashuo Zhang,Ruheng Wang,Leyi Wei
In an article, the researchers discuss the importance of accurately predicting protein-ligand interactions for drug discovery and design. They highlight the limitations of traditional methods and propose a novel deep learning model called MucLiPred to improve these predictions. The model utilizes dual contrastive learning mechanisms at both the residue and type levels. The residue-level contrastive learning helps identify binding and non-binding residues with precision, while the type-level contrastive learning focuses on the context of ligand types. By training the model to distinguish between different interaction motifs, it becomes proficient in recognizing global interaction patterns. The researchers found that MucLiPred outperforms existing models, with robust and accurate predictions. They also discovered that separating representation and classification tasks improves performance. Overall, MucLiPred is a groundbreaking tool for protein-ligand interaction prediction, with potential for further advancements in this field.
Original article: https://www.biorxiv.org/content/10.1101/2023.12.06.570503v1