Artificial Intelligence (AI) has rapidly become a transformative tool in pharmaceutical sciences, including the field of herbal drug research. The complexity of plant-based medicines, with multiple bioactive compounds and variable pharmacological actions, poses significant challenges in standardization, quality control, and formulation. AI technologies such as machine learning (ML), deep learning (DL), data mining, and predictive modeling are increasingly used to address these challenges by optimizing compound identification, activity prediction, formulation design, quality assessment, and safety profiling. This review discusses the current applications of AI in herbal drug research, key achievements, challenges, and future prospects. The integration of AI is shown to improve efficiency, accuracy, and reliability in research workflows, yet issues such as data scarcity, model interpretability, and regulatory acceptance remain key limitations. The review concludes with recommendations for advancing AI applications in herbal pharmacology.
Mr. Dinesh F. Choudhary*1, Mr. Harshwardhan S. Jadhav1, Ms. Kalyani U. Chande2 (Fri,) studied this question.