Phys-AbGAT: A Physics-Informed Multi-Task Graph Attention Network for Robust Antibody-Antigen Binding Affinity Prediction

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ID: 328071
2026
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Abstract
Abstract Motivation Accurate prediction of antibody-antigen binding affinity is essential for therapeutic antibody design. Existing methods often model the dissociation constant (KD) and Gibbs free energy (ΔG) independently, overlooking their thermodynamic relationship and complex paratope-epitope spatial interactions. Results We introduce Phys-AbGAT, a physics-informed multi-task graph attention network that represents the antibody-antigen interface as a spatial graph. It integrates ESM2 evolutionary embeddings with radial basis functions and employs a thermodynamically motivated auxiliary loss to encourage coupling between log10⁡(KD) and ΔG. On an independent 42-complex benchmark, Phys-AbGAT achieved Pearson correlation coefficients of 0.569 for log10⁡(KD) and 0.573 for ΔG. In matched-set comparisons, Phys-AbGAT achieved numerically lower RMSE and higher PCC than four baseline methods. The differences remained significant after Holm correction for MVSF-AB and PPA-Pred2, but not for AREA-AFFINITY or CSM-AB. Higher attention scores were assigned to several aromatic paratope residues, providing model-level hypotheses for structural inspection. These results support Phys-AbGAT as an interpretable multi-task geometric framework for joint prediction of two affinity-related quantities. Availability and implementation Code and data are available at https://github.com/cliffgao/Phys-AbGAT . Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7212109965 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yifei Yang, Linnan Xu, Junbiao Lu, Xiaoyan Li, Wei Zheng, Jianzhao Gao
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag669
URL
https://doi.org/10.1093/bioinformatics/btag669
Keywords Keywords not found

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