Integrating Phytochemistry and Artificial Intelligence for Natural Product Drug Discovery

Natural products continue to be an essential source of bioactive molecules for pharmaceutical development because of their remarkable structural diversity and wide range of biological activities. Traditional phytochemical drug discovery, however, is limited by the time consuming experimental process, the cost and the poor screening efficiency. The combination of phytochemistry and artificial intelligence (AI) has proved to be an effective approach to help identify, characterize and optimize therapeutic compounds in plants. The recent progress in AI-driven methodologies such as machine learning, deep learning, virtual screening, molecular docking, ADMET prediction, and multi-omics integration in the discovery of novel phytochemicals and their pharmacological potential is highlighted in this review. Computational applications are used synergistically to improve target identification, predict bioactivity, optimize for leads, and precision medicine, and lower drug development resources and time. Moreover, the current data quality, model interpretability, experimental validation, and clinical translation issues are discussed, alongside the novel opportunities enabled by explainable AI, generative AI, and autonomous drug discovery platforms. AI's ability to rapidly analyze vast amounts of data and identify novel compounds from natural sources alongside its ability to optimize their properties in a way that would be difficult for humans to achieve will create a paradigm shift in the study of natural products and in the development of safe, effective, and sustainable therapeutics in the future.