Artificial intelligence in predicting personalized nanocarrier formulations for herbal drugs: Bridging phytomedicine and precision nanotechnology
Duraisamy Sridhar, R. Manikandan, Yogananthan Dhanapal, Sulekha Khute, Paranthaman Subash · Intelligent Pharmacy · 2025
Herbal drugs offer significant therapeutic benefits but face challenges such as poor bioavailability, low stability, and variable patient responses. Nanocarrier-based delivery systems can overcome these limitations. However, their development often relies on time-consuming trial-and-error methods, which lack personalization. This review explores how advanced artificial intelligence (AI) models enhance nanocarrier formulation for phytoconstituents. These models include machine learning, deep learning, transformer-based models, and graph neural networks. They optimize nanocarrier selection, drug–carrier compatibility, and release profiles while incorporating patient-specific omics data. These computational tools accurately predict key parameters, such as particle size, drug loading, and release kinetics, for phytoactives like curcumin, quercetin, and genistein. Emerging applications include optimizing herbal nanocarriers for antimicrobial efficacy in infectious disease treatment. By integrating genomics, microbiome, and metabolomics data, these models enable tailored herbal nanomedicines. This data-driven approach, combining nanotechnology with phytomedicine, accelerates formulation, improves therapeutic efficacy, and advances precision herbal therapeutics. It also shows promise for infectious disease applications. Progress in explainable AI and supportive regulatory frameworks is essential for clinical translation.