BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge
  Bases
Authors: name: Mathew J. Koretsky, name: Maya Willey, name: Adi Asija, name: Owen Bianchi, name: Chelsea X. Alvarado, name: Tanay Nayak, name: Nicole Kuznetsov, name: Sungwon Kim, name: Mike A. Nalls, name: Daniel Khashabi, name: Faraz Faghri

Biomedical researchers increasingly rely on large-scale structured databases
for complex analytical tasks. However, current text-to-SQL systems often
struggle to map qualitative scientific questions into executable SQL,
particularly when implicit domain reasoning is required. We introduce
BiomedSQL, the first benchmark explicitly designed to evaluate scientific
reasoning in text-to-SQL generation over a real-world biomedical knowledge
base. BiomedSQL comprises 68,000 question/SQL query/answer triples grounded in
a harmonized BigQuery knowledge base that integrates gene-disease associations,
causal inference from omics data, and drug approval records. Each question
requires models to infer domain-specific criteria, such as genome-wide
significance thresholds, effect directionality, or trial phase filtering,
rather than rely on syntactic translation alone. We evaluate a range of open-
and closed-source LLMs across prompting strategies and interaction paradigms.
Our results reveal a substantial performance gap: GPT-o3-mini achieves 59.0%
execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%,
both well below the expert baseline of 90.0%. BiomedSQL provides a new
foundation for advancing text-to-SQL systems capable of supporting scientific
discovery through robust reasoning over structured biomedical knowledge bases.
Our dataset is publicly available at
https://huggingface.co/datasets/NIH-CARD/BiomedSQL, and our code is open-source
at https://github.com/NIH-CARD/biomedsql.