Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges

ABSTRACT

Medicinal plants represent a vast and evolutionarily refined reservoir of structurally diverse bioactive compounds that have historically contributed to major therapeutic breakthroughs. However, despite their pharmacological richness, systematic translation of plant‐derived metabolites into clinically approved drugs remains constrained by persistent bottlenecks, including extract complexity, dereplication redundancy, structural elucidation challenges, taxonomic ambiguity, and multi‐component pharmacology. This review presents a bottleneck‐driven and systems‐oriented framework for integrating artificial intelligence (AI) into medicinal plant‐based drug discovery (MPDD) to address some of these bottlenecks. Rather than reiterating broadly documented AI tools used in synthetic drug development, the manuscript critically examines phytomedicine‐specific challenges and maps AI applications across key stages of the pipeline: medicinal plant identification, extraction optimization, plant metabolite identification and dereplication, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, virtual screening, network pharmacology, repurposing, and generative de novo design of pseudo‐natural products. Emphasis is placed on the foundational requirement of making phytochemical datasets truly AI‐ready through metadata harmonization, dataset balancing, novelty‐aware modeling, and structured data engineering. Emerging approaches such as transformer‐based foundational models, graph neural networks (GNNs), generative AI (GAI) architectures, and multi‐omics‐integrated network pharmacology are discussed within a pragmatic translational context. Importantly, this review maintains a balanced perspective, acknowledging that fully AI‐driven clinically approved botanical drugs/synthetic drugs have yet to emerge and identifies challenges limiting the progress. By integrating computational methods with infrastructural reform, this work outlines a progressive roadmap to transition AI in medicinal plant research from exploratory studies toward standardized, reproducible, and biologically grounded next‐generation drug discovery.