THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN NATURAL PRODUCT CHEMISTRY RESEARCH

The integration of artificial intelligence (AI) into natural product chemistry represents a transformative paradigm shift in drug discovery and chemical research. Traditional approaches to natural product discovery—involving extraction, isolation, bioactivity screening, and structure elucidation—are time-consuming, resource-intensive, and limited by the vast chemical complexity of natural sources. AI technologies, including machine learning (ML) and deep learning (DL), are emerging as powerful tools that significantly enhance the speed, efficiency, and accuracy of natural compound discovery. This review critically examines current AI applications in natural product chemistry research, focusing on three key domains: bioactive compound prediction, complex spectral data interpretation, and de novo structure elucidation. Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. AI-driven tools enable the exploration of natural products from diverse sources, assist in optimizing high-throughput screening processes, and facilitate the identification of novel bioactive molecules. Machine learning has emerged as a popular tool for analyzing the structures of natural products. However, integrating AI in natural product research presents challenges including data quality issues, model interpretability, and the need for standardized benchmarks. This review synthesizes recent advances, critically appraises evidence on model generalizability, and outlines future directions for AI-driven natural product discovery