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DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

Xiaokun Li1,2,3, Qiang Yang4, Long Xu1

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DrugMGR, a novel deep learning model, enhances drug discovery by accurately predicting ligand-target interactions and binding sites. This computational approach improves cancer drug research by identifying potential compounds for clinical treatment.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Understanding ligand-target interactions is crucial for cancer drug optimization.
  • Existing computational methods have limitations in considering molecular representations and explicit binding sites.

Purpose of the Study:

  • To introduce DrugMGR, a deep multigranular drug representation model.
  • To predict binding affinities and interaction regions for ligand-target pairs.
  • To address limitations in current computational drug discovery methods.

Main Methods:

  • Developed a deep multigranular drug representation model (DrugMGR).
  • Utilized three benchmark datasets and a new dataset for validation.
  • Performed target-specific compound identification tasks for real-world screening.

Main Results:

  • DrugMGR achieved excellent performance in predicting binding affinities and regions.
  • The model demonstrated advantages over state-of-the-art methods.
  • Visualizations provided interpretable insights into interaction scenarios.

Conclusions:

  • DrugMGR effectively predicts ligand-target interactions and binding sites.
  • The model can be fine-tuned for identifying potential drug compounds for clinical applications.
  • DrugMGR offers a valuable tool to mitigate workloads in wet labs for cancer research.