Interaction-finder: automated literature-based discovery of biological entity associations with quote-level provenance

Identifying interactions between biological entities is a cornerstone of molecular research, but assembling such lists from the literature is slow and tedious. For many research questions, no curated database exists, leaving researchers to survey the relevant literature themselves. We present interaction-finder, a tool that automates this process: given a topic string and user-defined entity types, it discovers relevant literature through LLM-guided iterative search, extracts candidate associations from full-text articles, and produces a ranked list where every association is backed by quoted passages verified against the source text. A self-contained interactive HTML report enables rapid triage of the results. Evaluated across 60 topics in three domains (celltype-cellmarker, disease-gene, and ligand-receptor), interaction-finder recalls 1.2-4.3x as many known associations as single-shot prompting and an off-the-shelf deep-research framework, with all extracted quotes verified against source text. To assess candidates unrecognised from the gold-standard databases, we scored each candidate using an independent LLM judge blind to the tool's reasoning. Across the three domains, unverified candidates score similarly to gold-standard associations. We find the gold-standard associations are enriched at the top of our ranked candidates, with an overall recall@20 of 0.61. Interaction-finder is freely available at https://github.com/tecosaur/interaction_finder under an MIT licence.