HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction
For drug discovery researchers, HBGSA addresses the limitation of ignoring hydrogen bond spatial constraints in binding affinity prediction, offering improved accuracy for virtual screening.
HBGSA introduces a 3.06M-parameter model that encodes hydrogen bond spatial features using graph neural networks and self-attention, achieving superior binding affinity prediction on PDBbind Core Set and CSAR-HiQ datasets compared to baselines.
Accurate prediction of drug-target binding affinity accelerates drug discovery by prioritizing compounds for experimental validation. Current methods face three limitations: sequence-based approaches discard spatial geometric constraints, structure-based methods fail to exploit hydrogen bond features, and conventional loss functions neglect prediction-target correlation, a key factor for identifying high-affinity compounds in virtual screening. We developed HBGSA (Hydrogen Bond Graph with Self-Attention), a 3.06M-parameter model that encodes hydrogen bond spatial features. HBGSA uses graph neural networks to model hydrogen bond spatial topology with self-attention enhancement and Pearson correlation loss. Experimental results on PDBbind Core Set and CSAR-HiQ dataset demonstrate that HBGSA outperforms baseline methods with strong generalization capability. Ablation studies confirm the effectiveness of hydrogen bond modeling and Pearson correlation loss.