Explainable AI in Nanomedicine: Interpretable Models for Nano–Bio Interactions
Making nanomedicine understandable—one interpretable model at a time.
About This Course
This workshop explores the rapidly growing field of Explainable AI (XAI) in nanomedicine, focusing on how interpretable machine-learning models can unravel complex nano–bio interactions, cellular uptake mechanisms, toxicity patterns, and targeting behaviors. Participants will learn how transparency-driven AI approaches—such as SHAP, LIME, rule-based models, and interpretable neural networks—help scientists understand why nanoparticles behave the way they do inside biological systems. Through structured lectures and conceptual demonstrations, the workshop bridges nanotechnology, data science, and biomedical research to empower evidence-driven, safer, and clinically translatable nanocarrier design.
Aim
To train participants in the principles and applications of Explainable AI for understanding nano–bio interactions and guiding nanomedicine development.
Workshop Objectives
Participants will:
- Understand nano–bio interactions, cellular uptake, biodistribution, and toxicity pathways.
- Learn the fundamentals of Explainable AI (XAI) and interpretable machine learning.
- Explore model transparency tools such as SHAP, LIME, and feature importance analysis.
- Interpret AI predictions related to nanoparticle behavior and biological responses.
- Evaluate XAI frameworks for improving safety, efficacy, and regulatory acceptance of nanomedicine.
Workshop Structure
Day 1 — Foundations of Nano–Bio Interactions & Interpretable ML
- Fundamentals of nano–bio interactions
- Protein corona
- Cellular uptake pathways
- Biodistribution & organ targeting
- Toxicity mechanisms
- Need for interpretability in nanomedicine
- Overview of interpretable ML models
- Decision trees
- Logistic regression
- Rule-based classifiers
- Nanoparticle descriptors
Day 2 — Explainable AI Techniques & Interpretation Tools
- Explainable AI frameworks
- SHAP (Shapley values)
- LIME (Local Interpretable Model-Agnostic Explanations)
- Global vs. local explanations
- Feature importance analysis
- Visualizing nanoparticle behavior using XAI
- Conceptual Python walkthrough: interpreting a nanoparticle toxicity model
Day 3 — Applying XAI for Safer & Smarter Nanomedicine
- Using XAI to refine nanoparticle design
- Surface chemistry
- Core material type
- Size & charge
- Ligand density
- Case studies: interpreting nanoparticle biodistribution, uptake, toxicity
- XAI for regulatory science and risk assessment
- Future directions:
- Trustworthy AI
- Transparent nanomedicine pipelines
- Clinically interpretable predictive models
Who Should Enrol?
- UG & PG students in Biotechnology, Nanotechnology, Pharmacy, Biomedical Sciences, AI/ML
- PhD scholars focusing on nanomedicine, drug delivery, ML, toxicology, or materials science
- Academicians integrating AI with nanotechnology or biomedical research
- Industry professionals from pharma, biotech, diagnostics, data science, and regulatory domains
Important Dates
Registration Ends
01/21/2026
IST 07:00 PM
Workshop Dates
01/21/2026 – 01/23/2026
IST 08:00 PM
Workshop Outcomes
By the end of the workshop, participants will be able to:
- Explain the role of XAI in scientific decision-making in nanomedicine.
- Apply interpretability concepts to understanding nanoparticle datasets.
- Identify key nanoparticle descriptors influencing biological outcomes.
- Interpret model outputs related to toxicity, uptake, and biodistribution.
- Evaluate how XAI strengthens trust, reproducibility, and clinical translation in nanomedicine.
Fee Structure
Student Fee
₹1799 | $70
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Academician / Faculty Fee
₹3799 | $95
Industry Professional Fee
₹4799 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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