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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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PocketDTA: A pocket-based multimodal deep learning model for drug-target affinity prediction.

Jiang Xie1, Shengsheng Zhong1, Dingkai Huang1

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.

Computational Biology and Chemistry
|March 12, 2025
PubMed
Summary

PocketDTA, a novel deep learning model, enhances drug discovery by integrating protein structure and sequence data for accurate drug-target affinity prediction. This pocket-based approach improves model generalization for more reliable predictions.

Keywords:
Binding sitesDeep learningDrug–target affinityPocket

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Drug-target affinity prediction is vital for drug discovery.
  • Integrating protein structural information is challenging but crucial for prediction accuracy.
  • Existing models often lack spatial information due to reliance on sequence data alone.

Purpose of the Study:

  • To propose PocketDTA, a pocket-based multimodal deep learning model for improved drug-target affinity prediction.
  • To leverage protein structural information by introducing a pocket graph structure.
  • To enhance prediction accuracy and generalization by integrating multimodal data.

Main Methods:

  • Developed PocketDTA, a multimodal deep learning model utilizing a pocket graph structure.
  • Encoded protein residue features using a biological language model as nodes.
  • Employed relational graph convolutional networks at atomic and residue levels for feature extraction.
  • Integrated multimodal information from sequence and structural data.

Main Results:

  • PocketDTA demonstrated superior performance compared to state-of-the-art models on benchmark datasets.
  • The model showed strong generalization capabilities under realistic data splits.
  • Validated the effectiveness of pocket-based approaches for drug-target affinity prediction.

Conclusions:

  • PocketDTA effectively integrates multimodal data (sequence and structure) for enhanced drug-target affinity prediction.
  • The pocket graph structure successfully incorporates spatial information, overcoming limitations of sequence-only models.
  • Pocket-based multimodal deep learning represents a promising direction for advancing drug discovery.