Unlocking New Frontiers: The Role of Generative AI in Drug Discovery from Natural Products – Applications, Challenges, and Future Directions

Abstract

Natural products derived from plants, fungi, microorganisms, and other biological sources represent a valuable reservoir of structurally diverse and biologically active compounds for pharmaceutical discovery. However, conventional natural-product drug discovery is often time-consuming, resource-intensive, and challenged by complex chemical structures, limited data availability, and difficulties in compound isolation, optimization, and biological characterization. Generative artificial intelligence (GenAI) is emerging as a transformative approach that can complement conventional drug-discovery strategies by learning patterns from chemical and biological datasets and generating novel molecular structures inspired by natural products. This review explores the emerging applications, limitations, and future perspectives of GenAI in natural-product-based drug discovery.

Major applications include AI-driven compound generation using approaches such as variational autoencoders, generative adversarial networks, recurrent neural networks, transformers, diffusion models, and reinforcement learning; predictive modeling of biological activity; integration of genomics, proteomics, and metabolomics data; virtual screening; target identification; and lead optimization. Overall, the convergence of natural-product chemical diversity and generative AI offers a promising pathway to accelerate the discovery and development of innovative therapeutic candidates while complementing established experimental approaches.