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

Shunfan Li; Jiangkai Long; Xin Zou; Chang Tang; Yuanyuan Liu; Xiao He; Xuesong Yan

AAAI 2026technical0 citations

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

Shunfan Li, Jiangkai Long, Xin Zou, Chang Tang, Yuanyuan Liu, Xiao He, Xuesong Yan

Abstract

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.

BibTeX

@inproceedings{aaai2026_plamgramultigran,
  title = {PLA-MGRA: Multi-Granularity and Relation-Aware Learning for Efficient and Generalizable Protein-Ligand Binding Affinity Prediction},
  author = {Shunfan Li and Jiangkai Long and Xin Zou and Chang Tang and Yuanyuan Liu and Xiao He and Xuesong Yan},
  booktitle = {AAAI 2026},
  year = {2026}
}