Medicinal Plant Research: Current Advances, Challenges, and Future Directions

Abstract

Medicinal plants have been integral to the health care systems for centuries and are still a valuable source of bioactive compounds for developing new therapies. Although medicinal plants have a great potential in the field of medicine, the conventional approach of medicinal plant research is constrained by time consuming phytochemical isolation, lengthy experimental validation and high attrition during the drug development process. Thanks to recent developments in computing technology, this picture has changed to allow for the fast identification, characterization and optimization of potentially therapeutic phytochemicals. In this context, the application of AI, machine learning, CADD, molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, network pharmacology, multi-omics integration, and ADMET prediction has been shown to speed up the process of natural product-based drug discovery and decrease both drug development costs and timelines. Besides, the virtual screening and target identification through large-scale databases have been made possible by publicly available chemical and pharmacological databases, thereby enabling the discovery of complex mechanisms of herbal medicines. The integration of new technologies like deep learning, generative AI, and explainable AI is also further developing computational medicinal plant research, which is increasingly improving the precision of predictive models and the interpretability of the AI model. However, several obstacles such as the scarcity of good quality datasets, inconsistencies in the phytochemical databases, poor experimental validation, concerns over algorithm transparency, and many other challenges remain and hinder the wide adoption of these tools. It provides an overview of the latest development of computational platforms used for research on medicinal plants, their use and limitations, some representative examples of medicinal plant research applying computational approaches and what is needed for the integration of computational technologies with experimental pharmacology. Together, these computational strategies are revolutionizing medicinal plant research and speeding up the identification of plant-based therapies with improved safety, efficacy and economical viability.