AI for Predictive Nanotoxicology and Safe-by-Design Nanomaterials

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.

• 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.

📅 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

• 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.

• 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.