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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Navigating the Computational Landscape for Drug Repurposing.

Andrea Álvarez-Pérez1, Lucía Prieto-Santamaría1, Ana I Casas2,3

  • 1Centro de Tecnología Biomédica and ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Madrid, Spain.

Annual Review of Pharmacology and Toxicology
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

Drug repurposing finds new uses for old drugs. Computational methods, including AI, offer powerful tools for identifying these opportunities to treat diseases more effectively.

Keywords:
artificial intelligencecomputational methodologiesdrug repositioningdrug repurposingnetwork medicine

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Area of Science:

  • Pharmacology
  • Bioinformatics
  • Computational Biology

Background:

  • Drug repurposing is a cost-effective strategy for identifying new therapeutics.
  • Data-driven computational approaches are increasingly vital in drug repurposing research.
  • Existing knowledge bases and computational techniques provide a foundation for novel drug discovery.

Purpose of the Study:

  • To review computational methodologies for drug repurposing.
  • To highlight the role of artificial intelligence and large language models in this field.
  • To provide a resource for researchers in computational drug repurposing.

Main Methods:

  • Molecular docking simulations
  • Network-based computational methods
  • Omics data integration
  • Artificial intelligence and large language models

Main Results:

  • Computational approaches provide valuable insights into drug repurposing.
  • Case studies demonstrate the practical application of these methods.
  • AI and LLMs are opening new avenues for identifying repurposing candidates.

Conclusions:

  • Computational drug repurposing accelerates the discovery of new treatments.
  • Integration of diverse computational techniques enhances identification of repurposing opportunities.
  • Future directions involve leveraging advanced AI for drug repurposing challenges.