A unified knowledge graph linking foodomics to chemical-disease networks and flavor profiles

Abstract Modern nutrition science still lacks a comprehensive, machine-readable map linking diet to molecular composition and biological effects. Here we present FoodAtlas, a large-scale knowledge graph that links 1430 foods to 3610 chemicals, 2181 diseases, and 958 flavor descriptors through 96,981 provenance-tracked edges. A transformer-based text-mining pipeline extracted 48,474 quantitative food–chemical associations from 125,723 literature sentences ( F 1 = 0.67) and integrated them with 23,211 chemical–disease assertions from the Comparative Toxicogenomics Database, 15,222 chemical-bioactivity records from ChEMBL, 3645 flavor annotations from FlavorDB and PubChem, and 6429 taxonomic relationships. Graph embeddings revealed six dietary modules whose signature metabolites delineate distinct, multisystem disease-risk trajectories. Models built on FoodAtlas demonstrate practical utility: a bioactivity predictor achieved strong correlation with antioxidant assays ( R ² = 0.52; ρ = 0.72), and a substitution engine reduced simulated total disease risk by 11.9%.