Drug Discovery and Development from Ethnomedicinal Plants in the Artificial Intelligence Era

Herbal medicines are in great demand in primary healthcare in both developed and developing countries. Investigating natural sources, especially ethnomedicinal plants, has long been a mainstay in the field of drug development. Centuries of traditional knowledge have yielded a wealth of bioactive compounds, providing an invaluable basis for pharmacological innovation. In the modern world, the amalgamation of state-of-the-art technology, most notably artificial intelligence (AI), has transformed the procedure of unearthing new medications from ethnomedical plants. Research on ethnopharmacology has shown a wide range of plants with medicinal qualities, and native populations have preserved priceless information about these plants customary applications. In the realm of medicine quality control, artificial intelligence (AI) is a key component in improving the precision and effectiveness of a variety of quality assurance procedures. Utilizing AI techniques such as deep learning models, machine learning algorithms, and data analytics has greatly accelerated the process of identifying, isolating, and characterizing the bioactive chemicals from these plants. Technological developments in artificial intelligence (AI) enable the rapid screening of extensive chemical libraries, molecular interaction prediction, and drug–target interaction optimization. Combining artificial intelligence (AI) with molecular modeling methods and high-throughput screening accelerates the identification of potential drug candidates, hence simplifying the lengthy process of drug discovery. In addition, artificial intelligence algorithms facilitate the interpretation of intricate chemical structures, forecast pharmacokinetic characteristics, and refine formulations, thus augmenting the efficacy and triumph rate of drug discovery.