Revolutionizing Natural Product Discovery: The Role Of Generative AI

Abstract

The traditional, resource-intensive process of de novo natural product design, a cornerstone of drug discovery requiring extensive interdisciplinary knowledge, has been revolutionized by the advent of generative artificial intelligence (AI). This review comprehensively explores the integration of generative AI methodologies, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Recurrent Neural Networks (RNNs), in designing novel natural products with desired biological activities. These AI models facilitate efficient molecule generation and optimization, marking a significant shift from conventional approaches like combinatorial chemistry and high-throughput screening. We examine key methodologies encompassing data preparation, model training, and rigorous molecule validation, highlighting successful case studies in the discovery of antibiotics, anticancer agents, and antiviral compounds where AI-designed molecules demonstrate notable advantages. The review critically analyzes existing challenges such as data quality, model interpretability, the crucial integration with experimental validation, and ethical and regulatory considerations. Looking ahead, we discuss potential advancements in AI algorithms, the enhanced incorporation of biological data, collaborative research paradigms, and the potential impact of quantum computing on this field. In conclusion, generative AI offers transformative capabilities for de novo natural product design, significantly accelerating the discovery and optimization of new therapeutic compounds. Despite ongoing challenges, continuous progress in AI and computational chemistry positions generative AI as a pivotal tool in the future development of innovative therapeutics.