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Computational drug repositioning based on the relationships between substructure-indication.

Jingbo Yang1, Denan Zhang1, Lei Liu1

  • 1College of Bioinformatics Science and Technology, Harbin Medical University.

Briefings in Bioinformatics
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method for drug repositioning by analyzing drug substructures and protein domains, enabling prediction of diverse therapeutic effects and mechanisms. The approach successfully identified known and potential new drug indications, including antiviral effects for olaparib.

Keywords:
drug repositionlocal structural similaritysubstructure–domain associations

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Area of Science:

  • Computational drug discovery
  • Pharmacogenomics
  • Cheminformatics

Background:

  • Current drug repositioning methods often overlook local drug structures, limiting the discovery of new therapeutic functions.
  • Drug-target interactions are influenced by drug substructures and protein domains, suggesting a basis for novel prediction strategies.

Purpose of the Study:

  • To develop a new computational approach for drug repositioning by integrating chemical-genomics and pharmaco-genomics data.
  • To establish a drug-substructure-indication network for predicting all therapeutic effects based solely on drug substructure information.

Main Methods:

  • Integrated substructure-domain (chemical-genomics) and domain-indication (pharmaco-genomics) features.
  • Constructed a drug-substructure-indication network to identify relationships.
  • Validated predictions using multiple verification methods and literature review.

Main Results:

  • Generated 83,205 drug-indication relationships with associated correlation scores.
  • Successfully predicted known (antitumor) and novel (antiviral) indications for olaparib.
  • Elucidated olaparib's mechanism of inhibiting DNA repair via specific substructure interactions.

Conclusions:

  • The proposed method effectively predicts drug indications and mechanisms by focusing on local structural similarities.
  • This approach holds significant potential for uncovering additional therapeutic effects of existing drugs.
  • The drug-substructure-indication network provides a powerful tool for comprehensive drug effect prediction.