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React-to-Me: A Conversational Interface for Interactive Exploration of the Reactome Pathway Knowledgebase.

Helia Mohammadi1,2, Fatemeh Almodaresi3, Gregory F J Hogue1

  • 1Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.

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|December 22, 2025
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Summary
This summary is machine-generated.

React-to-Me is a new AI assistant that makes exploring biological pathways easier. It uses natural language to query the Reactome Pathway Knowledgebase, ensuring accurate and traceable scientific information.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Knowledge Representation

Background:

  • The Reactome Pathway Knowledgebase offers curated data on human biological pathways but has accessibility challenges for non-experts due to its complex data model and keyword search.
  • General conversational AI lacks the domain specificity, source transparency, and factual reliability needed for scientific research.

Purpose of the Study:

  • To develop a domain-specific conversational AI assistant, React-to-Me, for querying the Reactome Pathway Knowledgebase using natural language.
  • To enhance the accessibility and usability of complex biological pathway data for a wider range of users.

Main Methods:

  • Implemented a hybrid retrieval-augmented generation (RAG) approach combined with constrained language model generation.
  • Integrated semantic vector search with keyword-based matching for improved retrieval accuracy.
  • Ensured all AI-generated responses are grounded in curated Reactome content with direct links to knowledgebase entries.

Main Results:

  • Computational benchmarking showed that hybrid retrieval significantly improved contextual grounding and factual precision compared to dense-only methods.
  • Blinded expert evaluations revealed that grounded responses received higher quality ratings for factual accuracy, biological specificity, and mechanistic depth.
  • User surveys indicated high satisfaction with React-to-Me's ease of use, citation reliability, and factual accuracy.

Conclusions:

  • Domain-specific grounding markedly enhances the reliability and usability of conversational AI for biological knowledge exploration.
  • React-to-Me offers a transparent, scientifically robust, and user-friendly interface for accessing Reactome pathway information.
  • The developed system demonstrates the potential of specialized AI assistants in scientific research and data accessibility.