Artificial Intelligence in Natural Product Research: Transforming Drug Discovery and Precision Medicine

Abstract

Natural products remain important for the discovery of bioactive compounds for drug development; traditional screening and development are tedious, time-consuming, and expensive. Artificial Intelligence (AI) has transformed natural product research, enabling scientists to rapidly identify phytochemicals, predict molecular targets, perform virtual screening, perform molecular docking, perform ADMET property evaluation, and optimize leads. The processing of billions of chemical and biological data points is improved by machine learning, deep learning, generative AI, and explainable AI, which all help to improve the accuracy and efficiency of medication development. In addition, AI-powered multi-omics integration, pharmacogenomics, and clinical data are crucial for precision medicine, facilitating biomarker identification, personalized treatment strategies, and data-driven clinical decision-making. Progress has been made; however, there are challenges related to data quality, model interpretability, ethical concerns, and regulatory acceptance. Advancements in explainable and generative AI will accelerate the advancement of autonomous drug discovery and enhance the conversion of natural product therapeutics to the clinic. This review highlights recent progress, current challenges, and future perspectives of precision medicine from an AI perspective regarding natural products.