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CollDTI: Dual-encoder collaborative learning for drug-target interaction prediction.

Wanchen Li1, Junlin Xu2, Yajie Meng3

  • 1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 30, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces CollDTI, a novel deep learning framework for identifying drug-target interactions (DTIs). CollDTI effectively integrates structural and relational features, improving the accuracy of predicting potential DTIs for drug discovery.

Keywords:
Drug discoveryDrug-target interactionHeterogeneous graphMulti-view information

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Area of Science:

  • Bioinformatics
  • Computational Chemistry
  • Drug Discovery

Background:

  • Accurate identification of Drug-Target Interactions (DTIs) is crucial for drug discovery and development.
  • Existing deep learning methods struggle to effectively integrate intrinsic drug/target structural features with external relational information.

Purpose of the Study:

  • To propose CollDTI, a dual-encoder collaborative learning framework for enhanced DTI prediction.
  • To improve the integration of intrinsic and relational features for more accurate DTI identification.

Main Methods:

  • Constructed a multi-view heterogeneous graph neural network with a dual-view encoder for relational feature extraction.
  • Extracted intrinsic structural features using character dictionary encoding, graph convolutional networks, and skip-connections.
  • Employed a weighted residual cross-attention mechanism for collaborative fusion of multi-view embeddings.

Main Results:

  • CollDTI demonstrated superior performance compared to existing baseline models in DTI prediction.
  • Experimental results confirmed the effectiveness of integrating structural and relational features.
  • Visualizations and case studies validated the model's ability to identify potential drug-target interactions.

Conclusions:

  • CollDTI offers an effective approach for DTI prediction by synergistically combining intrinsic and relational features.
  • The framework enhances the reliability and accuracy of identifying potential drug-target interactions.
  • This work contributes to advancing computational methods in drug discovery and development.