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Knowledge Graphs for drug repurposing: a review of databases and methods.

Pablo Perdomo-Quinteiro1, Alberto Belmonte-Hernández1

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Knowledge Graphs (KGs) and artificial intelligence (AI) accelerate drug repurposing for new disease treatments. This review highlights KGs, AI techniques, and explainability for reliable drug discovery.

Keywords:
artificial intelligencedrug repurposingexplainabilitygraph networksknowledge graphs

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Area of Science:

  • Pharmacology
  • Biotechnology
  • Computer Science

Background:

  • Drug repurposing offers an efficient strategy for identifying novel therapeutic agents.
  • Knowledge Graphs (KGs) are increasingly utilized for discovering potential drug candidates.

Purpose of the Study:

  • To review prominent Knowledge Graphs (KGs) and their role in drug repurposing.
  • To explore artificial intelligence (AI) techniques that enhance drug repurposing efficiency and precision.
  • To emphasize the importance of explainability and validation in AI-driven drug repurposing.

Main Methods:

  • Comprehensive review of existing Knowledge Graphs (KGs), detailing their structure and data sources.
  • Exploration of various artificial intelligence (AI) techniques applied to drug repurposing.
  • Discussion of explainability methods and prediction validation strategies.

Main Results:

  • Knowledge Graphs (KGs) provide a robust framework for identifying drug repurposing candidates.
  • AI techniques significantly accelerate and improve the accuracy of drug repurposing predictions.
  • Explainability methods enhance the trustworthiness and transparency of AI-driven drug discovery.

Conclusions:

  • The integration of KGs and AI offers a powerful approach to drug repurposing.
  • Explainable AI (XAI) is crucial for validating and trusting AI-generated drug repurposing predictions.
  • Further research into validation techniques will ensure reliable and understandable drug discovery outcomes.