AI-Augmented Discovery of Natural Compounds for Multi-System Disease and Environmental Health: A Physiology-First Translational Framework

Natural products represent a vast and underutilized source of therapeutic candidates across multiple disease domains. However, traditional discovery approaches often prioritize isolated potency or target affinity without sufficient consideration of human physiology, delivery constraints, or translational feasibility. We present a physiology-first AI framework for natural compound discovery that integrates multi-source data, multi-model ranking, and human-guided arbitration to prioritize candidates with real-world therapeutic potential. The platform incorporates chemical databases, literature-derived signals, biological pathway mapping, and delivery-aware scoring to identify compounds with multi-system relevance. Early proof-of-concept work using natural product datasets demonstrates the ability to prioritize candidates with anti-inflammatory, antioxidant, and systems-level effects. Applications extend across neurodegenerative disease, cardiometabolic disorders, oncology, longevity, and environmental health. As an illustrative use case, the platform supports identification of interaction-mediated biologics targeting microplastic and nanoplastic burden, including polysaccharides and enzyme classes evaluated through ex vivo experiments. This framework provides a scalable pathway for translating natural compound discovery into clinically relevant therapeutic strategies.