HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913774486683648 |
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| author | Li, Jiannuo Yao, Lan |
| author_facet | Li, Jiannuo Yao, Lan |
| contents | Accurate prediction of the binding affinity between drugs and target proteins is a core task in computer-aided drug design. Existing deep learning methods tend to ignore the information of internal sub-structural features of drug molecules and drug-target interactions, resulting in limited prediction performance. In this paper, we propose a drug-target association prediction model HCAF-DTA based on cross-attention fusion hypergraph neural network. The model innovatively introduces hypergraph representation in the feature extraction stage: drug molecule hypergraphs are constructed based on the tree decomposition algorithm, and the sub-structural and global features extracted by fusing the hypergraph neural network with the graphical neural network through hopping connections, in which the hyper edges can efficiently characterise the functional functional groups and other key chemical features; for the protein feature extraction, a weighted graph is constructed based on the residues predicted by the ESM model contact maps to construct weighted graphs, and multilayer graph neural networks were used to capture spatial dependencies. In the prediction stage, a bidirectional multi-head cross-attention mechanism is designed to model intermolecular interactions from the dual viewpoints of atoms and amino acids, and cross-modal features with correlated information are fused by attention. Experiments on benchmark datasets such as Davis and KIBA show that HCAF-DTA outperforms state of the arts in all three performance evaluation metrics, with the MSE metrics reaching 0.198 and 0.122, respectively, with an improvement of up to 4% from the optimal baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks Li, Jiannuo Yao, Lan Biomolecules Artificial Intelligence Machine Learning Accurate prediction of the binding affinity between drugs and target proteins is a core task in computer-aided drug design. Existing deep learning methods tend to ignore the information of internal sub-structural features of drug molecules and drug-target interactions, resulting in limited prediction performance. In this paper, we propose a drug-target association prediction model HCAF-DTA based on cross-attention fusion hypergraph neural network. The model innovatively introduces hypergraph representation in the feature extraction stage: drug molecule hypergraphs are constructed based on the tree decomposition algorithm, and the sub-structural and global features extracted by fusing the hypergraph neural network with the graphical neural network through hopping connections, in which the hyper edges can efficiently characterise the functional functional groups and other key chemical features; for the protein feature extraction, a weighted graph is constructed based on the residues predicted by the ESM model contact maps to construct weighted graphs, and multilayer graph neural networks were used to capture spatial dependencies. In the prediction stage, a bidirectional multi-head cross-attention mechanism is designed to model intermolecular interactions from the dual viewpoints of atoms and amino acids, and cross-modal features with correlated information are fused by attention. Experiments on benchmark datasets such as Davis and KIBA show that HCAF-DTA outperforms state of the arts in all three performance evaluation metrics, with the MSE metrics reaching 0.198 and 0.122, respectively, with an improvement of up to 4% from the optimal baseline. |
| title | HCAF-DTA: drug-target binding affinity prediction with cross-attention fused hypergraph neural networks |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.02014 |