Ethnopharmacology, the study of traditional medicinal plant use within cultural contexts, has garnered significant attention for its potential to inspire novel therapeutics. Yet the diversity, heterogeneity, and context-dependence of ethnomedicinal knowledge pose substantial challenges for systematic pharmacological investigation. Traditional research methods struggle to keep pace with the explosive growth of ethnobotanical, chemical, and pharmacological datasets—and with the variable quality, provenance, and interoperability of those data.

Advanced computational tools, including artificial intelligence (AI) and big data analytics, are increasingly deployed to organize, integrate, and interrogate these complex resources. Recent studies have demonstrated promising capabilities—identifying bioactive compounds, prioritizing targets and indications, and flagging safety concerns. However, realizing reliable, reproducible, and equitable impact from AI requires overcoming critical hurdles that are often under-discussed: data quality and bias, uncertainty and ambiguity in model outputs, risks of hallucinated or spurious associations, limited external validity and mechanistic interpretability, weak linkage to experimental confirmation, and challenges in aligning traditional knowledge systems with modern ontologies and legal/ethical frameworks.

This Research Topic aims to advance the field by catalyzing both innovation and rigor in AI‑enabled ethnopharmacology. We invite contributions that not only showcase new methods and applications, but also systematically interrogate limitations, risks, and pathways to trustworthy translation from in silico insights to in vitro and in vivo evidence. We particularly welcome work that clarifies failure modes, quantifies uncertainty, and proposes practical mitigation strategies.

To gather further insights in this evolving and interdisciplinary domain, we welcome articles that focus specifically on the interface between artificial intelligence, big data analytics, and ethnomedicinal plant research. Contributions may address, but need not be limited to, the following themes:

Computational strategies for integrating ethnobotanical, phytochemical, and pharmacological data

Machine learning and deep learning methods for compound prediction and activity modeling

Data mining approaches to uncover hidden patterns in ethnomedicinal usage

Assessment strategies for AI-predicted pharmacological effects

Case studies of AI-driven drug discovery from traditional medicinal plants

Ethical, legal, and reproducibility considerations in big data ethnopharmacology

The application of network pharmacology and systems biology to ethnomedicinal research

Please note:

We encourage the submission of original research articles, reviews, methodological papers, perspectives, and case reports that address the challenges and opportunities at the intersection of AI, big data, and ethnomedicinal plant-based drug discovery.

Studies must follow the Four Pillars of Best Practice in Ethnopharmacology and ConPhyMP guidelines (Front. Pharmacol. 13:953205; https://ga-online.org/best-practice
), and both in experimental and AI-based studies musr include detailed information on plant material. For experimental studies the identification, extraction, and processing methods must be described in such detail that the studies are reproducible. Submissions based solely on in silico predictions of alleged ‘actives’ will not be considered (e.g. docking studies, network analyses). Such studies must include in vitro, in vivo, or clinical assessments.

Are you interested in contributing a manuscript to this Research Topic?

Artificial Intelligence and Big Data-Driven Discovery and Pharmacological Prediction of Ethnomedicinal Plants