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SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction.

Zhiqiang Hu1, Wenfeng Liu2,3, Chenbin Zhang1

  • 1SenseTime Research, Shanghai, 201103, China.

Briefings in Bioinformatics
|December 22, 2022
PubMed
Summary

This study introduces a novel drug-target binding affinity prediction model that excels without protein sequence data. By focusing on ligand interactions, the model achieves superior performance and generalizability across diverse protein targets.

Keywords:
deep learningdrug–target binding affinitysequence-agnostic model

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Drug-target binding affinity prediction is crucial for drug discovery.
  • Existing methods often process protein and ligand information separately.
  • A need exists for more robust and generalizable prediction models.

Purpose of the Study:

  • To develop a novel drug-target binding affinity prediction model.
  • To demonstrate superior performance without relying on protein sequence information.
  • To create a universally generalizable ligand representation across proteins.

Main Methods:

  • A multi-head training approach treating each protein separately.
  • Characterizing proteins solely by their interacting ligands.
  • Learning a robust and universal ligand representation.

Main Results:

  • The novel paradigm significantly outperformed sequence-based methods (MSE: 0.4261 vs. 0.7612, R-Square: 0.7984 vs. 0.6570).
  • The model showed robustness in transfer learning scenarios with unseen proteins.
  • Effective cross-dataset evaluation for prospective studies was demonstrated.

Conclusions:

  • Drug-target binding affinity can be accurately predicted without protein sequence information.
  • The proposed ligand-centric approach offers superior generalizability and predictive power.
  • The model shows promise for future drug discovery and prospective studies.