Plant-Derived Bioactives for Nutraceuticals: AI Screening, Nanoencapsulation & Formulation Optimization

The aim of this workshop is to equip participants with an integrated computational framework for the screening, characterization, nanoencapsulation, delivery modeling and AI-driven optimization of plant-derived bioactive compounds for nutraceutical applications.

Participants will learn how molecular descriptors, antioxidant activity data, release kinetics, nanocarrier characteristics and formulation variables can be transformed into predictive models that support the development of more stable, bioaccessible and scientifically optimized nutraceutical delivery systems.

• Screen and profile polyphenols, flavonoids, and plant antioxidants.
• Perform QSAR and antioxidant activity prediction for DPPH and ABTS.
• Understand nanoemulsions, liposomes, and solid lipid nanoparticles (SLNs).
• Model encapsulation efficiency and release kinetics.
• Analyze bioavailability and gastrointestinal bioaccessibility.
• Apply machine learning and Bayesian optimization to nutraceutical formulations.
• Evaluate particle size, PDI, zeta potential, and formulation stability.

• Perform computational phytochemical and antioxidant screening.
• Build basic QSAR and machine-learning models.
• Analyze nutraceutical nanocarrier and release data.
• Optimize formulations using XGBoost and Bayesian Optimization.
• Develop reproducible workflows for AI-assisted nutraceutical research.

• PhD Scholars and Researchers
• Academicians and Faculty Members
• Nutraceutical and Functional Food Researchers
• Pharmacy and Formulation Scientists
• Biotechnology and Biochemistry Researchers
• Food Scientists and Food Technologists
• Nanotechnology Researchers
• AI/ML and Computational Chemistry Researchers
• Nutraceutical and Pharmaceutical Industry Professionals
• Postgraduate students in Pharmacy, Biotechnology, Food Science, Chemistry, Nutrition, and Life Sciences