HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911622525616128 |
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| author | Kong, Junxiao Tang, Chupei Wang, Di Zhai, Jixiu He, Yi Tang, Moyu Lu, Tianchi |
| author_facet | Kong, Junxiao Tang, Chupei Wang, Di Zhai, Jixiu He, Yi Tang, Moyu Lu, Tianchi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23115 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction Kong, Junxiao Tang, Chupei Wang, Di Zhai, Jixiu He, Yi Tang, Moyu Lu, Tianchi Machine Learning 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. |
| title | HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.23115 |