Integrating Artificial Intelligence and Machine Learning in Natural Product Discovery: From Omics Data to Drug Design

Abstract

Natural products represent an essential source of bioactive materials with potent biological activity, traditional approaches for their discovery and development remain time consuming and resource-intensive. Recent advances of Artificial Intelligence (AI) and machine learning (ML) are transforming natural product research by enabling predictive modeling, high throughput screening, automated compound identification and integration of complex omics datasets. Other AI and ML methods such as deep learning, neural networks and generative algorithms are enhancing drug discovery by predicting bioactivity and structural elucidation, and optimizing pharmacokinetic properties of natural products. Furthermore, AI, ML, and other technologies are opening new possibilities, such as genome mining for biosynthetic gene clusters, promoting dereplication strategies, and integrating multi-omics datasets to understand natural product biosynthesis and pharmacology at the systems level. This review summarizes the recent applications of AI and ML in natural product discovery, metabolomics, cheminformatics and pharmacology, present highlighted successful case studies, associated challenges and future perspectives for AI-driven sustainable drug discovery.