AI-guided ethnopharmacology for cardiovascular drug discovery: from biomedical data to experimental validation

Despite substantial advances in pharmacological therapy, cardiovascular disease remains the leading cause of mortality worldwide. Botanical drugs represent a rich source of structurally diverse bioactive metabolites, yet their translation into modern cardiovascular therapeutics remains limited by chemical complexity, incomplete mechanistic understanding, and challenges in experimental validation. Meanwhile, the rapid expansion of biomedical data resources and advances in artificial intelligence (AI) have created new opportunities to systematically investigate botanical drugs beyond empirical approaches. In this review, we summarize the major biomedical data resources supporting AI-guided ethnopharmacology, including phytochemical and ethnomedicinal databases, multiomics datasets, single-cell and spatial transcriptomics, perturbation-response resources, biological interaction networks, and clinical evidence. We then discuss the computational approaches used for molecular property prediction, drug-target interaction prediction, perturbation-response modeling, and foundation-model transfer learning, highlighting how these complementary strategies address distinct stages of pharmacological discovery. Building on these advances, we propose two complementary AI-guided discovery frameworks: a compound-first strategy that predicts the pharmacological activities of botanical drugs and a disease-first strategy that identifies natural products capable of reversing disease-associated molecular and cellular states. We further discuss emerging applications in cardiovascular diseases, emphasizing the integration of AI with single-cell atlases, disease-state prediction, mechanistic investigation, and experimental validation to prioritize candidate botanical therapeutics. Finally, we outline current challenges in data standardization, model robustness, interpretability, and prospective validation. We propose that AI-guided ethnopharmacology should be viewed as a hypothesis-generation framework that complements, rather than replaces, pharmacological experimentation. When supported by standardized botanical materials, high-quality multimodal data, rigorous biological validation, and prospective clinical evaluation, AI has the potential to accelerate cardiovascular botanical drug discovery and facilitate the translation of ethnomedicinal knowledge into modern precision therapeutics.