BioLiP3: a protein-ligand affinity database integrating curated data with LLM-enabled literature mining

Quantitative binding affinities and three-dimensional protein-ligand structures provide complementary information essential for structure-based drug discovery, yet these data remain largely disconnected across existing databases and the biomedical literature. We present BioLiP3, a major extension of BioLiP that expands quantitative affinity coverage 13-fold within its structure-centered framework for biologically relevant interactions. BioLiP3 contains 593 423 affinity records, of which 591 820 are linked to three-dimensional protein-ligand structures: 45 386 experimentally resolved PDB complexes, 528 732 BindingDB-derived records with computationally generated structures, and 17 702 structure-linked records derived from LLM-mined biomedical literature. For records lacking experimental complex structures, we generate structural models using available experimental or predicted receptor structures, binding-site identification, and molecular docking, with experimental and computational structures explicitly distinguished. To complement existing databases, we developed a hybrid literature-mining workflow combining large language models with rule-based validation and independent verification to extract Ki, Kd, IC50, and EC50 measurements from PubMed abstracts while preserving provenance. BioLiP3 further integrates ligand-binding sites and protein, functional, and structural annotations via cross-references to PDB, UniProt, NCBI Taxonomy, PubChem, and original literature. Taken together, these advances make BioLiP3 a unified resource for data-driven protein-ligand interaction studies and structure-based drug design. BioLiP3 is freely accessible at https://zhanggroup.org/BioLiP/.