Artificial Intelligence in the Discovery of Bioactive Natural Products

Artificial intelligence (AI) is transforming natural products discovery by integrating genome mining, metabolomics, bioactivity prediction, and virtual screening into unified computational ecosystems. This review provides a comprehensive overview of AIdriven approaches for the discovery of bioactive natural products, covering AI-ready databases, machine learning tools for biosynthetic gene cluster (BGC) prediction, metabolomics platforms, NMR and MS spectral interpretation, and translational development. The convergence of AI with multi-omics technologies, synthetic biology, and automated experimentation is accelerating the identification and prioritization of novel therapeutic leads. Key challenges, including data quality, reproducibility, interpretability, and ethical considerations, are critically examined. The review concludes that AI functions most effectively as an integrative decision-support framework that augments rather than replaces experimental chemistry and pharmacology expertise.