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Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.

Betsabeh Tanoori1, Mansoor Zolghadri Jahromi2, Eghbal G Mansoori2

  • 1School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. betsatanoori@gmail.com.

Journal of Computer-Aided Molecular Design
|June 30, 2021
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Summary

This study proposes a novel semi-supervised transfer learning method to predict drug-target binding affinity. The approach treats drug-target interaction prediction as a regression problem, outperforming existing methods on benchmark datasets.

Keywords:
Binary interactionBinding affinity predictionDrug–target interactionGradient boosting machineSemi-supervised learningTransfer learning

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Area of Science:

  • Computational biology
  • Pharmacology
  • Machine learning

Background:

  • Drug-target interaction prediction traditionally uses binary classification.
  • This overlooks the inherent regression nature of binding affinity.
  • Existing methods lack continuous binding affinity data for training.

Purpose of the Study:

  • To develop a semi-supervised transfer learning approach for predicting drug-target binding affinity.
  • To address the scarcity of continuous binding affinity data in target domains.
  • To identify binary drug-target interactions based on binding strength.

Main Methods:

  • A semi-supervised transfer learning framework is proposed.
  • Leverages data from source domains to compensate for missing target domain data.
  • An objective function incorporating source, target, and unlabeled target data performance is optimized.
  • Gradient boosting machines are used for model construction.

Main Results:

  • The proposed regression model predicts binding affinity on a continuous spectrum.
  • The method effectively utilizes information from related domains.
  • Experimental results on benchmark datasets show superior performance compared to state-of-the-art methods.

Conclusions:

  • The developed approach offers a more realistic prediction of drug-target interactions.
  • Semi-supervised transfer learning is effective for binding affinity prediction with limited data.
  • The regression-based model enhances accuracy in identifying drug-target relationships.