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Sequence-based drug-target affinity prediction using weighted graph neural networks.

Mingjian Jiang1, Shuang Wang2, Shugang Zhang3

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266525, China.

BMC Genomics
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces WGNN-DTA, a novel graph neural network model for predicting drug-target affinity (DTA). WGNN-DTA accurately predicts binding affinity using protein and molecular graphs, offering a faster alternative for large-scale virtual screening.

Keywords:
Drug-protein affinity predictionGraph neural networkSequence representation

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Drug-target affinity (DTA) prediction is crucial for virtual screening and drug development.
  • Sequence-based DTA prediction is fast but lacks accuracy due to omitted structural information.
  • Improving DTA prediction accuracy is essential for efficient drug discovery.

Purpose of the Study:

  • To develop a novel computational model for accurate drug-target affinity (DTA) prediction.
  • To leverage protein and molecular structure information for enhanced DTA prediction.
  • To create a fast and accurate method suitable for large-scale virtual screening.

Main Methods:

  • Constructed protein and molecular graphs from sequence and SMILES data.
  • Utilized graph neural networks (GNNs) to extract structural features.
  • Developed the Weighted Graph Neural Networks drug-target affinity predictor (WGNN-DTA).

Main Results:

  • WGNN-DTA demonstrates high accuracy in drug-target affinity (DTA) and compound-protein interaction (CPI) prediction.
  • The model exhibits simplicity and superior performance across various experimental scenarios.
  • WGNN-DTA achieves fast execution speeds, suitable for screening large compound databases.

Conclusions:

  • WGNN-DTA effectively predicts binding affinity by utilizing structural information from protein and molecular graphs.
  • The proposed method offers a balance of simplicity, high accuracy, and computational efficiency.
  • WGNN-DTA provides a valuable tool for accelerating drug discovery through efficient virtual screening.