Mentor Based
Nanomedicine 4.0: AI-Enabled Nanomaterials for Drug Delivery, Diagnostics & Precision Therapeutics
Exploring the convergence of artificial intelligence and nanotechnology to design smarter medicines, advanced diagnostics, and personalized therapeutic solutions.
About This Course
Nanomedicine 4.0 represents the next generation of healthcare innovation, combining advanced nanomaterials with artificial intelligence for improved diagnosis, targeted drug delivery, and personalized treatment strategies. This workshop introduces participants to AI-driven approaches in nanocarrier design, nano-biosensing, therapeutic prediction, and nanotoxicology through conceptual learning and hands-on data-driven workflows.
Aim
To provide participants with an understanding of how AI and machine learning approaches are transforming nanomedicine by enabling intelligent nanomaterial design, drug-delivery optimization, nano-diagnostics, toxicity prediction, and precision therapeutic development.
Workshop Objectives
- Understand the fundamentals of nanomedicine and AI-enabled nanotherapeutic approaches.
- Explore different nanomaterials and their applications in drug delivery and diagnostics.
- Learn how AI/ML models support nanocarrier optimization and therapeutic prediction.
- Analyze biomedical datasets related to nano-drug delivery, biomarkers, and toxicity assessment.
- Gain practical exposure to AI-assisted workflows for precision nanomedicine applications.
Workshop Structure
Day 1: AI-Enabled Nanomaterials for Drug Delivery
Key Concepts
- Introduction to Nanomedicine 4.0 and the evolution of AI-assisted nanomedicine
- Major nanocarrier platforms: Lipid nanoparticles, Polymeric nanoparticles, Liposomes, Dendrimers, Metallic nanoparticles, Nanogels
- Physicochemical properties influencing drug delivery: Particle size, Surface charge, Morphology, Stability, Drug-loading efficiency
- Passive and active targeting strategies
- Stimuli-responsive and controlled drug-release systems
- Role of AI/ML in: Nanocarrier design, Formulation optimization, Drug-delivery performance prediction
Special Hands-On
- Explore a nanomaterial/drug-delivery dataset
- Perform data preprocessing and visualization
- Analyze relationships between nanoparticle properties and delivery efficiency
- Build a basic ML model for predicting nanocarrier performance
- Compare candidate nanomaterials for drug-delivery suitability
Day 2: AI-Powered Nano-Diagnostics & Biosensing
Key Concepts
- Role of nanotechnology in modern disease diagnostics
- Nano-biosensors and point-of-care diagnostic platforms
- Functional nanomaterials: Gold nanoparticles, Quantum dots, Magnetic nanoparticles, Nanosensors
- Biomarker detection and signal enhancement strategies
- Nanomaterials in biomedical imaging
- Integration of nanotechnology with molecular and clinical datasets
- AI/ML approaches for: Diagnostic classification, Biomarker interpretation, Nano-bio interaction prediction
Special Hands-On
- Explore biomarker/biosensor datasets
- Perform exploratory data analysis and feature selection
- Develop a basic diagnostic classification workflow
- Evaluate: Accuracy, Sensitivity, Specificity, ROC-AUC
- Interpret key diagnostic features using explainable AI concepts
Day 3: AI-Guided Precision Nanotherapeutics & Nanotoxicology
Key Concepts
- Precision medicine approaches using nanotechnology
- AI-guided selection of nanocarriers and therapeutic strategies
- Theranostic nanoparticles: Integrated diagnosis and therapy platforms
- Nano-enabled cancer therapeutics
- Gene, RNA, and targeted delivery applications
- AI-assisted nanotoxicology: Cytotoxicity prediction, Biological response assessment, Safety evaluation
- Challenges in clinical translation of AI-enabled nanomedicine
- Future perspectives: Generative AI in nanomedicine, Personalized nanotherapeutic design
Special Hands-On
- Analyze nanotoxicity/therapeutic-response datasets
- Build a basic AI prediction workflow for: Toxicity assessment or Therapeutic response prediction
- Compare nanomaterial candidates using multiple parameters
- Visualize:Model predictions or Feature importance
- Develop an AI-assisted nanomedicine decision framework
Core Hands-On Toolkit:
Python + Google Colab + Pandas + Scikit-learn + SHAP + Biomedical Nanomaterial Datasets
Who Should Enrol?
- Undergraduate and postgraduate students from Biotechnology, Nanotechnology, Biomedical Sciences, Life Sciences, Pharmacy, Chemistry, and related fields.
- PhD scholars and researchers working in nanomedicine, drug delivery, biomaterials, diagnostics, and computational biology.
- Faculty members and professionals interested in AI-driven healthcare innovation.
- Basic knowledge of biology, nanotechnology, or data analysis is recommended.
Important Dates
Registration Ends
October 1, 2026
IST 4:30 PM
Workshop Dates
October 1, 2026 – October 3, 2026
IST 5:00 PM
Workshop Outcomes
- Explain the role of nanomaterials in modern drug delivery and healthcare applications.
- Evaluate nanoparticle properties influencing therapeutic performance.
- Apply basic AI/ML workflows for nanomaterial analysis and prediction.
- Understand AI-based approaches for nano-diagnostics and biomarker classification.
- Interpret toxicity and therapeutic response prediction models.
- Develop an overview of AI-assisted decision frameworks for personalized nanomedicine.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $85
Industry Professional Fee
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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