PLA-MGRA: Multi-Granularity and Relation-Aware Learning for Efficient and Generalizable Protein-Ligand Binding Affinity Prediction

Protein-Ligand Affinity (PLA) prediction quantifies the interaction strength to guide rational drug design. Existing approaches typically analyze interaction at a single granularity and overlook tightly coupled relationships between protein and ligand in both structure and functionality, consequently yielding suboptimal representations, leading to significant performance drops in real-world scenarios. To address this problem, we propose PLA-MGRA, a minimalist and effective PLA prediction framework. Specifically, PLA-MGRA captures both fine-grained atomic details and coarse grained functional semantics within the 3D structure of protein–ligand complexes, through multi-granularity learning. To further parse the coupled protein–ligand relationships, we design relation-aware learning to enhance the binding nature of representations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple protein–ligand affinity prediction benchmarks, while also offering generalizability and interpretability.

Authors

• Shunfan Li China University of Geoscience, Wuhan, China
• Jiangkai Long China University of Geoscience, Wuhan, China
• Xin Zou Wuhan University
• Chang Tang China University of Geosciences
• Yuanyuan Liu School of Artificial Intelligence, Xidian University, China
• Xiao He State Key Laboratory of Integrated Services Networks, Xidian University
• Xuesong Yan China University of Geoscience, Wuhan, China

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