Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Abstract excerpt

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve...

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