Artificial Intelligence-Driven Drug Delivery Systems for Herbal Therapeutics: Computational Strategies Bridging Phytopharmaceutical Traditional and Precision Nanomedicine - Singh Gurjar, Dharmendra; Singh, Balveer; Raj Sharma, Yash; Kapoor, Ritu; Adlakha, Manoj (Journal of Neonatal Surgery, 2025)
Background: Herbal therapeutics possess substantial pharmacological assertion, their clinical translation is though repeatedly hindered by poor aqueous solubility, low oral bioavailability, phytochemical instability and compositional variations across batch to batch. Also, the site -specific action related data is largely absent. Artificial Intelligence (Ai) is highly positioned to solve these prolonged formulational voids. Aim: The current review solidifies current evidence on how machine learning, Deep learning and predictive modelling are applied across phytopharmaceutical delivery pipeline from bioactive identification to nano-carrier optimisation, and to examine the points of convergence with Ayurvedic Pharmaceutical reasoning. Methods: A non-computational conceptual literature synthesis was caried out using PubMed, ScienceDirect, Scopus and Google Scholar covering available literature from 2015 to 2026 analysing the published findings on AI-assisted phytochemical screening, nanocarrier design and herbal drug delivery alongwith classical Ayurvedic Text correlation. Results: In the literature, quantitative structure-activity relationship modelling, graph neural networks, digital twin-based process optimisation and simulation of release functions and estimation of toxicity of nanoparticles are mentioned as examples of the ways in which AI techniques can accelerate the discovery of bioactive phytoconstituents, predict the compatibility of the drugs with the carriers, and simulate the release function and estimate the toxicity of the nanoparticles. Some improvements reported are: formulation screening is faster; encapsulation efficiency can be predicted early; problematic physicochemical interactions with the formulation can be identified early for all types of liposomal, phytosomal, polymeric and hydrogel formulations. Conclusion: AI has the potential to revolutionize phytopharmaceutical engineering and create more standardized and personalized delivery systems for herbs, but it is a pre-clinical field that still needs to be further explored and developed. Data on the beneficial phytochemicals that they cultivate, streamlined regulatory paths and future clinical trials will be crucial to their improvement. The Rasa Panchakas are a concept suggested for material characterisation in the classical ayurvedic formulation and are tentatively proposed to be used in feature design...