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From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation

arXiv 2603.00612 ↗First posted 28 Feb 2026cs.CLcited by 0

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Abstract (arXiv)

The rapid growth of biomedical evidence makes it difficult to translate biomarker mechanisms into actionable drug combination hypotheses. We present CoDHy, an interactive AI co-scientist for biomarker-guided hypothesis generation in oncology. CoDHy constructs task-specific knowledge graphs from curated databases and biomedical literature, then combines graph embeddings with agent-based reasoning to generate, validate, and rank evidence-grounded drug combinations. Through a web interface, researchers specify the biomarker, cancer context, and literature scope; inspect supporting evidence and intermediate results; and iteratively refine the generated hypotheses. The demonstration presents CoDHy's end-to-end workflow and shows how researchers can interactively explore and compare mechanistically supported drug combinations while remaining in control of hypothesis prioritization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 28 Feb 2026.

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References · 16

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4 more
  1. 2020AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approachcited by 21
  2. 2017MOLIERE: Automatic Biomedical Hypothesis Generation Systemcited by 11
  3. 2025Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directionscited by 9
  4. 2025BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generationcited by 3

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