BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

Abstract

Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical semantics distributed across text, tables, and figures, but also reconstructing chemically exact ligand structures (e.g., Markush structures). To address this bottleneck, we introduce BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW, a multi-modal extraction framework that explicitly separates bioactivity semantic interpretation from ligand structure construction. Within BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm, in which multi-modal large language models operate on chemically grounded visual representations to infer inter-structure relationships, and exact molecular construction is delegated to domain chemistry tools. For rigorous evaluation and method development, we further establish BO_SCPLOWIOC_SCPLOWVO_SCPLOWISTAC_SCPLOW, a comprehensive benchmark comprising 16,457 bioactivity entries curated from 500 publications. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW validates its extraction ability and provides a quantitative baseline, achieving an F1 score of 0.32 for bioactivity triplets. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOWs practical utility is demonstrated via three applications: (1) extracting 82,262 data from 11,683 papers to build a pre-training database that improves downstream models performance by 3.9%; (2) enabling a human-in-the-loop workflow that doubles the number of high-quality NLRP3 bioactivity data, helping 38.6% improvement over 28 QSAR models and identification of 16 hit candidates with novel scaffolds; and (3) accelerating protein-ligand complex bioactivity annotation, achieving a 5.59-fold speed increase and 5.75% accuracy improvement over manual workflows in PoseBusters dataset. BO_SCPLOWIOC_SCPLOWMO_SCPLOWINERC_SCPLOW and BO_SCPLOWIOC_SCPLOWVO_SCPLOWISTAC_SCPLOW provide a scalable extraction methodology and a rigorous benchmark, paving the way to unlock bioactivity data that previously required extensive human effort. All data and code are available at GitHub.