Bioprospecting 4.0 of Tropical Medicinal Plants: Integration of Metabolomics, Ethnobotanical Databases and Artificial Intelligence for the Robust Identification of New Bioactive Molecules

Abstract

Tropical medicinal plants constitute an important source of structurally diverse metabolites with potential value for pharmaceutical, nutraceutical and biotechnological research. Conventional natural-product discovery pipelines, however, remain limited by slow metabolite identification, fragmented ethnobotanical information and restricted translation from screening to validated candidates. This review examines Bioprospecting 4.0 as an integrated framework that combines untargeted metabolomics, ethnobotanical databases, artificial intelligence, cheminformatics and systems biology for the prioritisation of bioactive molecules from tropical medicinal plants. Particular attention is given to LC-MS/MS-based metabolomics, molecular networking, NMR-supported structural elucidation, knowledge graphs and AI-assisted prediction of biological targets and ADMET properties. The review also considers the role of explainable artificial intelligence, uncertainty quantification and active learning in improving interpretability, reproducibility and confidence in candidate selection. In addition, the manuscript discusses multi-omics integration, systems pharmacology, synthetic biology and translational validation as complementary components of next-generation bioprospecting. Ethical governance, FAIR data principles, biodiversity conservation and Access and Benefit-Sharing frameworks are addressed as essential requirements for responsible research involving genetic resources and associated traditional knowledge. Overall, the review highlights that Bioprospecting 4.0 can support a more systematic, transparent and ethically grounded exploration of tropical plant chemodiversity, while recognising the continuing challenges of data fragmentation, limited infrastructure, underrepresented biodiversity and predictive uncertainty.