When Herbs Meet Algorithms: Artificial Intelligence in Natural Drug Discovery

The discovery of herbal drugs has traditionally relied on ethnobotanical knowledge and experimental screening; however, these approaches are often time-consuming, resource-intensive, and limited in their ability to explore the vast chemical diversity of medicinal plants. Recent advances in artificial intelligence (AI) and computational methodologies have transformed natural product research by enabling faster, data-driven, and more precise identification of bioactive phytoconstituents. This integrative review critically examines the role of AI-based and in-silico approaches in the discovery and development of herbal drugs. Key computational techniques, including machine learning, deep learning, molecular docking, pharmacophore modelling, quantitative structure–activity relationship (QSAR) analysis, and network pharmacology, are discussed in the context of herbal medicine research. The review highlights how these tools facilitate target identification, activity prediction, toxicity assessment, and optimization of lead phytochemicals, while reducing experimental cost and failure rates. Additionally, the integration of big data resources such as phytochemical databases, omics platforms, and traditional medicine repositories is explored, emphasizing their contribution to predictive modelling and multi-target drug discovery. Challenges related to data quality, model interpretability, standardization of herbal datasets, and regulatory acceptance are also addressed. By bridging traditional herbal knowledge with modern computational intelligence, AI-driven approaches offer a promising pathway for accelerating herbal drug discovery and supporting evidence-based development of safe and effective phytopharmaceuticals. This review underscores the potential of AI and computational tools to reshape the future of herbal medicine research and innovation.Top of Form