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Nanotechnology · Moderate · Video-based learning

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.

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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.

Course 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.
Next: Syllabus →

Syllabus

Module 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

Module 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

Module 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.

Course 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.
Next: Fees →

Course fee

Learners in India

₹2,499

Learners outside India

$50

  • Video lectures
  • Study material
  • Certificate after completion
  • 365-day access

Enrol in the course

The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

How is this course taught?

Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.

How long do I have access?

365 days from your registration, on your NanoSchool dashboard.

Do I get a certificate?

Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
 WhatsApp +91 99581 61117

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