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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Related Experiment Video

Updated: Sep 5, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.

Daniel N Sosa1, Russ B Altman2

  • 1Department of Biomedical Data Science, Stanford University, 443 Via Ortega, 94305, California, USA.

Briefings in Bioinformatics
|July 11, 2022
PubMed
Summary

Artificial intelligence (AI) can accelerate drug discovery by analyzing complex biological data. This review explores using AI and knowledge graphs to overcome challenges in drug repurposing for unmet medical needs, especially rare diseases.

Keywords:
drug repurposingknowledge graphsmetasciencenatural language processing

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Area of Science:

  • Pharmacology
  • Biomedical Informatics
  • Artificial Intelligence

Background:

  • Rising drug development costs hinder treatments for unmet clinical needs, particularly rare diseases.
  • Artificial intelligence (AI) offers potential for discovering novel therapeutic options.
  • Biomedical literature-derived knowledge graphs (KGs) represent complex scientific information.

Purpose of the Study:

  • To address challenges in generating drug repurposing hypotheses using KGs.
  • To explore methods for incorporating context and resolving contradictions in scientific KGs.
  • To improve the accuracy and utility of AI-driven drug discovery.

Main Methods:

  • Framing drug repurposing hypothesis generation as a link prediction problem in KGs.
  • Discussing the semantic richness and up-to-date nature of literature-derived KGs.
  • Investigating methods for extracting contextual information (e.g., pharmacokinetics, pharmacodynamics, tissue specificity).

Main Results:

  • Inference on scientific KGs can be complicated by unspecified contexts and contradictions.
  • Contextual information is crucial for understanding drug-protein-gene-disease interactions.
  • Contradictions arise from omitted contexts or conflicting research claims.

Conclusions:

  • Overcoming challenges in KG construction is key for effective AI-driven drug discovery.
  • Incorporating context and resolving contradictions are essential for reliable pharmacological knowledge representation.
  • Advanced KG inference methods can enhance drug repurposing and address rare disease treatments.