MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.1093/bib/bbae238
Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbae238
- https://academic.oup.com/bib/article-pdf/25/3/bbae238/57743156/bbae238.pdf
- OA Status
- gold
- Cited By
- 16
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4397050505