ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN MODERN PHARMACOGNOSY: EXPANDING THE FRONTIERS OF NATURAL DRUG DISCOVERY

Abstract

Pharmacognosy, the scientific discipline concerned with drugs obtained from natural sources is using the combination of machine learning (ML) and artificial intelligence (AI) to evolve quickly. These technologies provide advanced computational approaches for managing large and complex datasets related to medicinal plants, phytochemicals, and biological activities. AI-based systems support plant identification, phytochemical profiling, bioactivity prediction, toxicity assessment, and molecular modeling. Machine learning algorithms enable predictive modeling and virtual screening, thereby accelerating natural product–based drug discovery . In addition, AI contributes to quality control, standardization, and formulation development of herbal medicines, ensuring safety, efficacy, and reproducibility . The convergence of pharmacognosy, data science, and pharmaceutical technology represents a major shift toward a more predictive, efficient, and sustainable approach to natural drug research and development.