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COLDLNA: Enhancing long-range node features extraction to improve robust generalization ability of drug-target binding affinity prediction in cold-start scenarios.

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ASAP-DTA: Predicting drug-target binding affinity with adaptive structure aware networks.

Weibin Ding1, Shaohua Jiang1, Ting Xu1

  • 1College of Information Science and Engineering, Hunan Normal University Changsha, Hunan 410081, P. R. China.

Journal of Bioinformatics and Computational Biology
|February 17, 2025
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This study introduces a new graph deep learning model for predicting drug-target affinity (DTA). The model improves accuracy in identifying drug targets, reducing wasted resources.

Keywords:
Drug-target affinity predictionadaptive structure aware networksgraph neural networksself-attention pooling

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning

Background:

  • Drug-target affinity (DTA) prediction is vital for drug repurposing and resource optimization.
  • Current methods face challenges in effectively capturing complex molecular representations.

Purpose of the Study:

  • To develop a novel graph-based deep learning model for accurate DTA prediction.
  • To enhance feature extraction from molecular graphs using adaptive structure-aware pooling and self-attention.

Main Methods:

  • A graph neural network integrated with a self-attention mechanism for node significance.
  • Adaptive structure-aware pooling (global and hierarchical) for molecular graph processing.
  • Clustering adjacent nodes and weighting features by attention scores for molecular representation.

Main Results:

  • Achieved the lowest mean squared error (MSE) of 0.126 on the KIBA dataset, a 0.5% improvement over baseline.
  • Demonstrated superior performance in both regression and binary classification tasks, validating generalization capabilities.

Conclusions:

  • The proposed model offers a significant advancement in graph feature extraction for DTA prediction.
  • The model's effectiveness and generalization potential are confirmed across benchmark datasets and task types.