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Natural products and their derivatives have long been crucial in drug therapy, especially in traditional medicine. However, challenges in screening, isolation, characterization, and optimization have slowed their development in the pharmaceutical industry. Recent advancements in artificial intelligence (AI) and multi-omics technologies are revitalizing this field. AI offers powerful tools for understanding natural compounds, enhancing molecular representations, and supporting tasks such as binding prediction, drug repurposing, and retrosynthesis. Moreover, generative models are aiding in natural product optimization and the creation of pseudo-natural compounds. At the same time, multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have enabled high-throughput studies of plant traits, synthesis, regulatory mechanisms, and quality control, providing valuable data for AI model development. These advancements help accelerate the discovery of new compounds with medicinal potential. Furthermore, in the field of traditional Chinese medicine research, which is largely based on natural plant sources, AI systems exemplified by UNIQ system, combining AI and multi-omics, have been instrumental in mechanistic studies and new drug development. This study comprehensively discusses the algorithms and applications of AI and multi-omics technologies in the drug development of natural compounds and plants, as well as summarizing relevant databases which might provide high-quality data for the future development of AI algorithms targeting natural products. AI and multi-omics transform natural product drug discovery, boosting innovation while tackling key issues in compound study and plant research.
Wang et al. (Mon,) studied this question.