About Workshop
AI for Predictive Nanotoxicology and Safe-by-Design Nanomaterials is a 3-day live masterclass focused on using AI and machine learning to assess nanomaterial toxicity and support safer material design. Participants will learn how to curate nanotoxicology datasets, extract chemical descriptors, build ML and deep learning models, and interpret toxicity predictions using Explainable AI.
The workshop covers Nano-QSAR modeling, applicability domain analysis, GNN-based nanomaterial representation, generative Safe-by-Design approaches, and regulatory informatics. Through hands-on labs using Google Colab, Python, RDKit, PyTorch, SHAP, LIME, and Streamlit, learners will develop practical workflows for toxicity screening, safety prediction, and AI-enabled nanomaterial optimization.
Aim
To equip participants with practical AI and machine learning skills for predictive nanotoxicology, Nano-QSAR modeling, toxicity risk assessment, and Safe-by-Design nanomaterial development.
What Participants Will Learn
- Understand key nanomaterial properties linked to toxicity.
- Curate and standardize nanotox datasets from sources such as NanoCommons and PubChem.
- Use RDKit to generate molecular descriptors and feature vectors.
- Build supervised ML models for early toxicity screening.
- Design Nano-QSAR workflows with feature scaling and cross-validation.
- Apply deep learning and GNN concepts for cytotoxicity prediction.
- Use SHAP and LIME to interpret toxicity-driving variables.
- Explore generative AI and active learning for Safe-by-Design nanomaterials.
- Build an interactive Streamlit dashboard for safety margin prediction and visualization.
Structure
📅 Day 1: Nanomaterial Data Engineering & Core ML
- Nanotoxicology fundamentals: core size, composition, morphology, surface charge, and cellular toxicity mechanisms
- Data curation: parsing heterogeneous nanotox repositories such as NanoCommons and PubChem into standardized formats
- Chemical informatics: utilizing RDKit to convert chemical structures and SMILES strings into feature vectors
- Supervised ML baselines: implementing Random Forests, SVMs, and Gradient Boosting for early toxicity screening
- Visualizing nanomaterial properties, assay outcomes, and structural-toxicity relationships for predictive modeling
- Preparing curated datasets for downstream Nano-QSAR and machine learning workflows
- Tools covered: Google Colab, Python, Pandas, NumPy, Matplotlib, RDKit, NanoCommons API
Hands-on Activity:
- Clean raw nanoparticle assay logs, programmatically extract RDKit descriptors, and visualize structural-toxicity correlations for predictive modeling
📅 Day 2: Advanced Nano-QSAR & Deep Learning
- Nano-QSAR architecture: designing statistically rigorous Quantitative Structure-Activity Relationship workflows
- Applicability domain: establishing mathematical boundaries for predictions to prevent false positives
- Deep learning: implementing Multi-Layer Perceptrons for cell viability and cytotoxicity prediction
- Graph Neural Networks: representing surface-functionalized nanomaterials as spatial graph structures
- Model validation strategies for toxicity prediction, endpoint classification, and risk interpretation
- Using neural network outputs to classify multi-endpoint nanotoxicity risks
- Tools covered: Google Colab, Python, Scikit-learn, PyTorch, PyTorch Geometric, DeepChem, Seaborn
Hands-on Activity:
- Code a neural network in PyTorch to map structural graph inputs, execute message-passing loops, and classify multi-endpoint toxicity risks
📅 Day 3: Generative Safe-by-Design & XAI Dashboards
- Safe-by-Design: shifting workflows from passive post-synthesis testing to proactive structural prevention
- Generative AI: utilizing Variational Autoencoders and reinforcement learning to optimize material safety
- Explainable AI: implementing SHAP and LIME frameworks to isolate critical toxicity-driving variables
- Regulatory informatics: mapping predictive model outputs to global safety standards and FDA approval workflows
- Designing AI-assisted workflows for safer nanomaterial screening and structural modification
- Building interactive dashboards for material profiling, toxicity prediction, and model explainability
- Tools covered: Streamlit, Plotly, PyTorch, VAEs, SHAP, LIME, GitHub-based repositories
Hands-on Activity:
- Create an interactive Streamlit dashboard that profiles a target material, predicts safety margins, visualizes SHAP feature importance, and suggests non-toxic structural modifications
Important Dates
Registration Ends
4.30 pm
Workshop Dates
2026-06-24
05:30 PM
05:30 PM
What You Will Gain
Outcomes
- Prepare nanotoxicology datasets for machine learning workflows.
- Extract RDKit descriptors from chemical structures and SMILES inputs.
- Train ML models such as Random Forest, SVM, and Gradient Boosting for toxicity prediction.
- Understand Nano-QSAR modeling and applicability domain concepts.
- Build basic deep learning models for cell viability and cytotoxicity prediction.
- Represent nanomaterials using graph-based AI concepts.
- Interpret model predictions using SHAP and LIME.
- Design Safe-by-Design AI workflows for safer nanomaterial development.
- Create a Streamlit dashboard for safety margin prediction, SHAP visualization, and structural modification suggestions.
Who Should Attend
- Students and PhD scholars in nanotechnology, biotechnology, toxicology, pharmacy, materials science, and biomedical engineering.
- Researchers working on nanomaterials, nanomedicine, cytotoxicity, or material safety.
- Academicians and faculty interested in AI-enabled nanotoxicology research.
- Industry professionals in pharma, biotech, materials R&D, regulatory science, and safety assessment.
- AI, ML, and data science learners interested in scientific and biomedical applications.