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SGcCA: Deciphering Drug-Target Interactions through an End-to-End Model with Spatial and Channel Reconstruction

Lihong Peng1, Wen Liao1, Zejun Li2

  • 1School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, Hunan, China.

Journal of Chemical Information and Modeling
|October 9, 2025
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SGcCA, a novel framework, enhances drug repositioning by accurately predicting drug-target interactions (DTIs) using deep learning. It outperforms existing models in DTI prediction, offering a valuable tool for researchers.

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Area of Science:

  • Computational biology
  • Drug discovery
  • Bioinformatics

Background:

  • Drug-target interaction (DTI) prediction is crucial for drug repositioning, but traditional methods are costly and time-consuming.
  • Deep learning offers advanced capabilities for DTI prediction, yet challenges remain in feature extraction and fusion.
  • Existing DTI prediction models face limitations in accurately learning and integrating drug and protein representations.

Purpose of the Study:

  • To introduce SGcCA, an end-to-end framework for enhanced DTI prediction.
  • To improve the accuracy and efficiency of DTI prediction for drug repositioning applications.
  • To address limitations in drug and protein feature learning and fusion in DTI prediction.

Main Methods:

  • Developed SGcCA, integrating Spatial and Channel reconstruction Convolution (SCConv), Graph Convolutional Network (GCN), and Cross-efficient-additive Attention (CEAA).
  • Utilized SCConv for encoding drug (SMILES) and protein (amino acid sequences) features by reducing redundancies.
  • Employed GCN for drug feature extraction from 2D molecular graphs and CEAA for effective feature fusion.

Main Results:

  • SGcCA demonstrated superior performance over six established DTI prediction models across four datasets (Human, C.elegans, BindingDB, DrugBank).
  • The framework achieved higher accuracy, F1-score, MCC, AUROC, and AUPRC, indicating improved interpretability and generalization.
  • Ablation studies confirmed the significant contributions of SCConv, CEAA, and GCN components; molecular docking validated predicted interactions.

Conclusions:

  • SGcCA provides a robust and effective solution for DTI prediction, significantly advancing drug repositioning efforts.
  • The framework's superior performance and interpretability make it a valuable tool for identifying novel drug-target interactions.
  • SGcCA is available as an open-source tool to support the drug discovery and repositioning community.