AI-Driven Design of Nanoparticles for Precision Drug Delivery
Integrating Artificial Intelligence with Nanotechnology for Next-Generation Precision Therapeutics
About This Course
This three-day intensive workshop (1.5-hour lecture per day) introduces participants to the integration of Artificial Intelligence and nanotechnology for precision drug delivery. The program covers nanoparticle systems such as Liposome, Polymeric Nanoparticle, and Gold Nanoparticle, and demonstrates how AI and machine learning tools like TensorFlow, Scikit-learn, and RDKit can accelerate nanoparticle design, optimization, and predictive modeling. Participants will gain conceptual understanding and practical insights into computational drug delivery modeling, data-driven formulation optimization, and translational pathways toward precision therapeutics.
Aim
To equip participants with interdisciplinary knowledge and practical skills to apply AI and machine learning techniques in designing and optimizing nanoparticles for precision drug delivery applications.
Workshop Objectives
- Understand nanoparticle systems used in precision medicine.
- Explore structure–property relationships in nanoparticle drug delivery.
- Apply machine learning models for predicting drug loading and release profiles.
- Learn AI-driven optimization techniques for nanoparticle formulation.
- Analyze biomedical datasets using Python-based tools.
- Understand regulatory and translational aspects including guidance from the U.S. Food and Drug Administration.
- Explore emerging trends in AI-powered personalized nanomedicine.
Workshop Structure
Day 1: Foundations of Nanoparticle Drug Delivery & Data Preparation
- Nanoparticle Drug Delivery Overview: Types (liposomes, polymeric nanoparticles, metallic nanoparticles, dendrimers) and clinical relevance
- AI in Nanomedicine: Role of machine learning in formulation optimization and precision delivery
- Understanding Nanoparticle Datasets: Key parameters (particle size, zeta potential, PDI, drug loading efficiency, encapsulation efficiency)
- Data Preprocessing: Cleaning formulation datasets, handling missing values, normalization, feature scaling
- Feature Engineering: Encoding surface chemistry, polymer types, integrating drug molecular descriptors
- Tools: Python, Pandas, NumPy, RDKit, Matplotlib, Jupyter/Colab
- Mini Task: Clean and prepare a nanoparticle formulation dataset and generate engineered features for modeling
Day 2: Predictive Modeling & Drug–Nanocarrier Interaction Analysis
- Machine Learning Fundamentals for Nanomedicine: Regression vs classification, algorithm selection
- Model Development: Random Forest, Gradient Boosting for predicting drug loading efficiency
- Model Evaluation: Cross-validation, R², RMSE, feature importance interpretation
- Drug–Nanocarrier Docking: Principles of molecular docking in nanocarrier systems
- Docking Workflow: Ligand preparation, receptor/nanocarrier modeling, binding affinity analysis
- Tools: Scikit-learn, DeepChem (optional), AutoDock Vina, PyMOL
- Mini Task: Build a predictive ML model for drug loading and perform docking to analyze drug–carrier interaction
Day 3: AI-Based Formulation Optimization & Research-Grade Reporting
- Formulation Optimization: Multi-objective tuning (loading efficiency, stability, release profile)
- Hyperparameter Tuning: GridSearchCV and model comparison
- NanoQSAR Concepts: Intro to toxicity and biocompatibility prediction
- Interpreting AI Outputs: Feature importance, prediction confidence, formulation ranking
- Reproducibility & Reporting: Best practices for workflow documentation and publication-ready visualization
- Tools: Scikit-learn tuning modules, Matplotlib, Jupyter/Colab, (optional) TensorFlow/Keras overview
- Generate an AI-optimized nanoparticle formulation shortlist and prepare a 1-page research-style computational report including model metrics and docking insights
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/18/2026
IST 07:00 PM
Workshop Dates
02/18/2026 – 02/20/2026
IST 08:00 PM
Workshop Outcomes
By the end of the workshop, participants will be able to:
- Explain the role of AI in nanoparticle design and precision drug delivery.
- Build basic predictive models for formulation optimization.
- Interpret computational outputs for drug release and targeting efficiency.
- Evaluate nanoparticle datasets using visualization and statistical tools.
- Identify translational challenges and ethical considerations in AI-driven healthcare.
- Propose AI-assisted strategies for personalized therapeutic delivery.
Fee Structure
Student Fee
₹1699 | $65
Ph.D. Scholar / Researcher Fee
₹2699 | $75
Academician / Faculty Fee
₹3699 | $85
Industry Professional Fee
₹4699 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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