Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.23115 [cs.LG] |
| (or arXiv:2604.23115v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.23115 arXiv-issued DOI via DataCite (pending registration) |
From: Tianchi Lu [view email]
[v1]
Sat, 25 Apr 2026 02:53:34 UTC (11,476 KB)
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