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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.

Yilu Fang1, Patrick Ryan1,2, Chunhua Weng1

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|May 24, 2024
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Generative AI and real-world evidence (RWE) built a drug indication taxonomy from labels. Large Language Models excel at concept hierarchies but struggle with term relations, a challenge for humans too.

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Area of Science:

  • Computational linguistics
  • Bioinformatics
  • Artificial Intelligence in Medicine

Background:

  • Drug labels contain crucial but unstructured indication information.
  • Automating the extraction and organization of this information is essential for drug discovery and pharmacovigilance.
  • Existing methods for taxonomy construction are often manual and time-consuming.

Purpose of the Study:

  • To develop an automated method for constructing a drug indication taxonomy.
  • To leverage generative Artificial Intelligence (AI), specifically Large Language Models (LLMs) like GPT-4, and real-world evidence (RWE) for this task.
  • To evaluate the performance of the AI-driven taxonomy against domain expert expectations.

Main Methods:

  • Extracted 2909 drug indication terms from 46,421 drug labels using GPT-4.
  • Iteratively generated indication concepts and inferred subsumption relations using GPT-4 integrated with RWE.
  • Constructed a hierarchical drug indication taxonomy.
  • Performed quantitative and qualitative evaluations with domain experts for cardiovascular, endocrine, and genitourinary diseases.

Main Results:

  • Created a drug indication taxonomy with 24 high-level categories and detailed sub-taxonomies (e.g., 242 concepts in the cardiovascular disease sub-taxonomy).
  • The taxonomy covers 234 indication terms associated with 189 drugs.
  • GPT-4 achieved >0.7 accuracy in determining drug indication hierarchy with good inter-rater reliability.
  • Concept-to-term subsumption relation checking showed fair to moderate reliability.

Conclusions:

  • Generative AI (LLMs) and RWE can successfully create drug indication taxonomies consistent with expert expectations.
  • LLMs are adept at deriving concept hierarchies but face challenges in determining concept-to-term subsumption relations in free-text labels.
  • The limitations in relation checking mirror difficulties faced by human experts.