AI-Assisted Discovery of Natural Bioactive Compounds

Abstract

Natural bioactive compounds derived from plants, microorganisms, and marine organisms have long been important sources for drug discovery. However, traditional discovery methods are slow, expensive, and limited by complex chemical diversity and biological variability. Artificial intelligence (AI) has recently emerged as powerful tool to accelerate natural product research by enabling efficient compound identification, activity prediction, biosynthetic pathway analysis, and lead optimization. AI techniques such as machine learning, deep learning, and data mining can analyze large chemical and biological datasets to discover new bioactive molecules with therapeutic potential. This review discusses role of AI in natural bioactive compound discovery, including data sources, molecular representation, predictive modeling, virtual screening, and systems biology integration. Applications in antimicrobial, anticancer, and anti-inflammatory compound discovery are summarized. Challenges including data quality, interpretability, and integration with experimental validation are also highlighted. AI-assisted natural product discovery can significantly reduce time and cost in drug development and enable identification of novel therapeutic agents from nature.

KEYWORDS: Artificial intelligence, natural products, bioactive compounds, machine learning, drug discovery, virtual screening, phytochemicals