Chapter 2: AI for Nutraceuticals
Synopsis
Author
Dr. Nayyar Parvez,
Professor & HOD, Department of Pharmaceutics, School of Pharmacy,
Sharda University, Greater Noida, Uttar Pradesh, India
Abstract
The field of nutraceuticals is undergoing a computational revolution, utilizing machine learning to accelerate the discovery and personalization of bioactive compounds, moving beyond traditional trial-and-error methods. AI algorithms streamline the screening process by virtually docking massive natural product libraries against disease-specific protein targets, identifying potent candidates without the resource constraints of physical assays. Quantitative Structure-Activity Relationship (QSAR) models refine these discoveries by correlating complex molecular topology with biological efficacy, while advanced predictive engines decipher the precise molecular mechanisms of action through network pharmacology. In the domain of personalization, AI facilitates the distinct customization of supplement formulations by integrating individual health datasets, including genetic predispositions (nutrigenomics) and biomarker deficiencies, to tailor combinations that address specific physiological needs. Optimization systems determine the precise dosage and lipid-based delivery vehicles required to maximize bioavailability, utilizing generative models to propose novel synergistic mixtures that enhance therapeutic outcomes. The safety and efficacy of these interventions are rigorously assessed through in silico toxicology models (ADMET) and the retrospective analysis of real-world clinical data. These predictive analytics preemptively identify potential nutraceutical-drug interactions, such as cytochrome P450 inhibition, ensuring that functional food development aligns with rigorous safety standards and delivers verifiable health benefits to the consumer.
Keywords: Virtual Screening, QSAR Modeling, Target Deconvolution, ADMET Profiling, Personalized Formulation, Bioavailability Optimization
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