About Workshop
This 3-day hands-on workshop explores plant-derived bioactive compounds, antioxidants, nanoencapsulation, and AI-driven nutraceutical formulation. Participants will learn computational screening, QSAR modeling, release kinetics, bioaccessibility analysis, and formulation optimization using RDKit, SwissADME, SciPy, XGBoost, and Bayesian Optimization.
Aim
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.
What Participants Will Learn
- 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.
Structure
🗓️ Day 1: In Silico Profiling & Screening of Plant-Derived Antioxidants
- Identifying and structurally classifying key plant-derived polyphenols, flavonoids, and other antioxidant phytochemicals.
- Evaluating antioxidant mechanisms and relating molecular features to radical-scavenging activity.
- Benchmarking drug-likeness, gastrointestinal absorption, bioavailability, and blood-brain barrier permeability.
- Programmatically querying and retrieving molecular structures, physicochemical properties, and bioactivity information.
- Performing virtual molecular screening to predict bioactivity and structural parameters without wet-lab delays.
- Developing Structure-Activity Relationship (SAR) models linking chemical descriptors with radical-scavenging efficiency.
- Building machine learning workflows to predict DPPH and ABTS antioxidant-scavenging activity.
🧰 Tools
RDKit, SwissADME , PubChem , Scikit-learn🧪 Hands-on Lab (Google Colab)
- Notebook: Automated QSAR Profiling and Antioxidant Potential Prediction Using RDKit and SwissADME Data.
- Task: Generate molecular descriptors, evaluate ADME properties, and train predictive models for plant-derived antioxidant compounds.
- Deliverable: Produce a ranked antioxidant candidate dataset with predicted DPPH/ABTS activity, molecular descriptors, and bioavailability profiles.
🗓️ Day 2: Nanocarrier Engineering & Drug Release Kinetics
- Designing nutraceutical delivery systems using liposomes, nanoemulsions, and solid lipid nanoparticles (SLNs).
- Understanding how nanoencapsulation protects labile antioxidants against pH, thermal, light, oxidative, and enzymatic degradation.
- Calculating encapsulation efficiency, loading capacity, retention rate, and bioactive release performance.
- Modelling dissolution and release profiles using Zero-Order, First-Order, Higuchi, and Korsmeyer-Peppas kinetic models.
- Differentiating Fickian, anomalous, and non-Fickian diffusion mechanisms within smart delivery matrices.
- Simulating antioxidant release behaviour during gastric and intestinal phases of in vitro gastrointestinal digestion.
- Comparing kinetic models using goodness-of-fit parameters to identify the dominant release mechanism.
🧰 Tools
Python, SciPy , NumPy , Matplotlib🧪 Hands-on Lab (Google Colab)
- Notebook: Multi-Model Nanocarrier Release Kinetics and Diffusion Mechanism Analysis Using SciPy and Python.
- Task: Fit antioxidant release data to Zero-Order, First-Order, Higuchi, and Korsmeyer-Peppas models and estimate kinetic parameters.
- Deliverable: Generate fitted release curves, model-comparison metrics, release exponents, and an interpretation of the dominant diffusion mechanism.
🗓️ Day 3: AI-Driven Formulation Optimization & Stability Profiling
- Characterizing nanocarrier formulations using Particle Size, Polydispersity Index (PDI), Zeta Potential, and encapsulation-efficiency parameters.
- Optimizing bioaccessibility to improve intestinal permeability, antioxidant retention, and cellular absorption profiles.
- Applying machine learning to formulation science as a predictive alternative to conventional trial-and-error and Response Surface Methodology (RSM) approaches.
- Performing multi-objective optimization to simultaneously minimize particle size and PDI while maximizing encapsulation efficiency and stability.
- Using predictive analytics to evaluate accelerated stability, degradation patterns, aggregation, and phase-separation behaviour under stress conditions.
- Applying Bayesian optimization to efficiently identify high-performing nanocarrier formulation parameters.
- Translating academic formulation datasets and predictive models toward commercially relevant nutraceutical development benchmarks.
🧰 Tools
XGBoost ,Bayesian Optimization,Scikit-learn,Python Visualization Libraries🧪 Hands-on Lab (Google Colab)
- Notebook: Multi-Objective Nanoemulsion Optimization and 3D Response Surface Modelling Using XGBoost & Bayesian Optimization.
- Task: Train predictive models and optimize formulation variables against particle size, PDI, encapsulation efficiency, and stability objectives.
- Deliverable: Generate an optimized nanocarrier formulation, predicted performance metrics, stability profile, and 3D response-surface visualizations.
Important Dates
Registration Ends
10 September 2026 4:30 PM
Workshop Dates
10 September 2026
5:30 PM
What You Will Gain
Outcomes
- 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.
Who Should Attend
- 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