Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges
4 affiliations
- 1Department of Pharmacognosy SVKM NMIMS Global University School of Pharmacy and Technology Management Dhule Maharashtra India
- 2Department of Pharmaceutical Chemistry SVKM NMIMS Global University School of Pharmacy and Technology Management Dhule Maharashtra India
- 3Department of Pharmacognosy, Krishna Institute of Pharmacy Krishna Vishwa Vidyapeeth (Deemed to be University) Karad Maharashtra India
- 4Department of Pharmaceutical Quality Assurance Gurunanak College of Pharmacy Nagpur India
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.
Related
- Integrating artificial intelligence and molecular simulation for peptide discovery: limitations, validation and decision-centered workflows
- Fractal-Inspired Biorefineries for Sustainable Polymer Production: A Conceptual Framework Integrating Artificial Intelligence and Industry 5.0
- Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives
- Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications
BibTeX
@article{gangwal2026integrating,
title = {{Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges}},
author = {Amit Gangwal and Azim Ansari and Mohd Usman Mohd Siddique and Jyotiram Arjun Sawale and Suhas P. Padmane and Raju Ratan Wadekar},
journal = {Chemical Biology \& Drug Design},
year = {2026},
month = jul,
doi = {10.1111/cbdd.70372},
url = {https://doi.org/10.1111/cbdd.70372},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges
AU - Amit Gangwal
AU - Azim Ansari
AU - Mohd Usman Mohd Siddique
AU - Jyotiram Arjun Sawale
AU - Suhas P. Padmane
AU - Raju Ratan Wadekar
PY - 2026
DA - 2026/07/31
JO - Chemical Biology & Drug Design
DO - 10.1111/cbdd.70372
UR - https://doi.org/10.1111/cbdd.70372
AB - 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.
SN - 1747-0277
SN - 1747-0285
ER -