Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Abstract

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical origin, cultivation, harvesting, processing, extraction, phytochemical composition, and storage, all of which contribute to batch-to-batch variation in biological activity and formulation performance. The presence of multiple co-occurring constituents further complicates the prediction of pharmacological activity, stability, bioavailability, and therapeutic response, while conventional herbal formulation development remains largely dependent on empirical, trial-and-error experimentation that becomes inefficient as the number of interacting formulation and process variables increases.Artificial intelligence (AI) — encompassing machine learning, deep learning, artificial neural networks, computer vision, natural language processing, quantitative structure–activity/property relationship modelling, graph neural networks, and generative approaches — offers an emerging, data-driven framework for addressing these limitations. This review critically examines AI applications spanning the herbal formulation-development continuum, including medicinal-plant authentication, phytochemical fingerprint interpretation, bioactive-compound prioritisation, herb–compound–target and network pharmacology analysis, polyherbal synergy prediction, nanocarrier selection and optimisation, Quality-by-Design and critical-quality-attribute prediction, dissolution and release modelling, stability prediction, quality control and adulteration detection, ADME and toxicity screening, and personalised phytopharmaceutical development. Particular attention is given to the convergence of AI with nanotechnology, given the poor aqueous solubility, limited permeability, and instability characteristic of many phytoconstituents. Despite this progress, translation into routine practice remains constrained by small and heterogeneous datasets, herbal batch variability, inconsistent metadata reporting, limited external validation, domain shift, model interpretability, and evolving regulatory frameworks. Future progress will depend on standardised datasets, explainable and physics-informed AI, automated closed-loop experimentation, multimodal data integration, digital twins, and prospective experimental validation. AI should therefore be regarded as a decision-support technology that complements, rather than replaces, pharmaceutical experimentation in herbal formulation science