DBGT-PLA: Dual-Branch Graph-Transformer Fusion for Interpretable Protein- Ligand Affinity Prediction

Protein-ligand binding affinity prediction is critical for drug discovery, yet existing methods struggle to jointly model local atomic interactions and global contextual dependencies. To address this, we propose the Interpretable Dual-Branch Graph-Transformer framework for Protein-Ligand Affinity prediction (DBGT-PLA), a novel dual-branch architecture that integrates graph neural network (GNN) with a stability-enhanced Transformer equipped with learnable positional embeddings and a NaN-filtering mechanism that handles potential Not-a-Number (NaN) values arising from numerical instability or data preprocessing. We design a Gated Residual Learning (GRL) Fusion module that performs dimension-wise adaptive integration between local graph topology and global Transformer context. This mechanism enables multi-level feature coordination through a residual path, achieving biophysically consistent alignment between atomic-level interactions and global conformational dependencies. Furthermore, we introduce an edge-level Shapley attribution framework tailored to protein-ligand interaction graphs, quantifying contributions of chemical bonds (e.g., hydrophobic contacts) and non-covalent interactions. Experiments show DBGT-PLA reduces RMSE by 18.3% (from 1.522 to 1.244 on the Holdout Set 2019), outperforming state-of-the-art models. Crucially, our explainability module reveals that the ligand edges dominate affinity predictions, accounting for nearly 70%. This work not only advances predictive accuracy but also offers unprecedented, quantitative insights into interaction determinants, which can guide rational drug optimization.