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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features.

Shourun Pan1, Leiming Xia1, Lei Xu1

  • 1College of Computer Science and Technology, Qingdao University, Qingdao, China.

BMC Bioinformatics
|September 7, 2023
PubMed
Summary

This study introduces a novel deep learning model for drug-target affinity (DTA) prediction. The model effectively integrates drug substructure information and multi-scale protein features, enhancing prediction accuracy in drug discovery.

Keywords:
Drug–target binding affinityMulti-scale featuresMutual informationSelf-supervised learning

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Drug-target affinity (DTA) prediction is crucial for drug discovery.
  • Deep learning methods are increasingly used for DTA prediction.
  • Existing methods face challenges in integrating drug substructure and multi-scale protein information.

Purpose of the Study:

  • To propose a novel self-supervised pre-training model for DTA prediction.
  • To effectively fuse drug molecular substructure information.
  • To leverage multi-scale features of proteins.

Main Methods:

  • For drugs: Probability matrix for substructure extraction and contrastive learning on graph and subgraph representations.
  • For targets: BiLSTM integrating multi-scale features to capture long-range dependencies.
  • Self-supervised pre-training of a graph encoder.

Main Results:

  • The model demonstrated improved performance in DTA prediction.
  • Effective integration of substructure and multi-scale features was achieved.
  • Enhanced accuracy in predicting drug-target interactions.

Conclusions:

  • The proposed model offers a novel strategy for DTA prediction.
  • Substructure extraction and multi-scale features significantly improve prediction performance.
  • This approach advances computational drug discovery.