AI-Assisted Predictive Toxicology: QSAR, ADMET, hERG & Organ-Toxicity Screening for Drug Safety
From Molecular Structure to Safety Prediction — Use AI and QSAR to Predict Drug Toxicity, ADMET Risks and Organ-Specific Safety Profiles.
About This Course
This 3-day mentor-led workshop introduces participants to AI-assisted predictive toxicology for early-stage drug safety assessment. Participants will learn how molecular structure and chemical descriptors can be transformed into predictive models for toxicity, ADMET properties, hERG liability, and organ-specific risks. The workshop combines QSAR, cheminformatics, machine learning, molecular descriptors, model interpretation, and virtual screening to demonstrate how computational approaches can support safer compound prioritisation before experimental validation.
Aim
To provide practical, research-oriented training in AI-assisted predictive toxicology, enabling participants to build and interpret computational workflows for QSAR, ADMET, hERG liability, and organ-toxicity screening in drug discovery.
Workshop Objectives
- Understand the principles of predictive toxicology and QSAR in modern drug discovery.
- Generate and curate molecular descriptors and chemical fingerprints for toxicity modelling.
- Develop machine-learning models to predict toxicity and key ADMET properties.
- Assess potential hERG-related cardiac liability using computational approaches.
- Screen compounds for potential hepatotoxicity, nephrotoxicity, neurotoxicity, and general toxicity.
- Apply explainable AI to identify molecular features contributing to predicted toxicity.
- Integrate QSAR, ADMET, hERG, and organ-toxicity predictions into a unified safety profile.
- Prioritise drug candidates based on predicted safety and risk profiles.
- Interpret model performance, applicability domains, uncertainty, and limitations.
- Build a reproducible AI-assisted workflow for early-stage computational drug-safety assessment.
Workshop Structure
📅 Day 1: QSAR, Molecular Descriptors & AI-Based Toxicity Prediction
Focus: Transform chemical structures into machine-learning-ready datasets and develop predictive QSAR models for toxicity assessment.
Topics Covered
- Introduction to predictive toxicology and its role in drug discovery
- Structure–toxicity relationships and QSAR principles
- SMILES, molecular structures, fingerprints, and molecular descriptors
- Physicochemical and structural descriptors for toxicity modelling
- Dataset curation, endpoint definition, and data-quality considerations
- Handling missing data, duplicates, outliers, and class imbalance
- Feature selection and dimensionality reduction
- Classification and regression approaches for toxicity prediction
- Random Forest, XGBoost and other ML approaches
- Model evaluation using accuracy, precision, recall, F1-score, ROC-AUC, MAE and RMSE
- Applicability domain and limitations of predictive models
🛠️ Hands-on
Build a QSAR toxicity dataset from chemical structures, generate molecular descriptors/fingerprints, train an ML model, evaluate its performance, and predict toxicity for new compounds.
Workflow:
Chemical Structures → Curation → Molecular Descriptors → Feature Selection → QSAR/ML Model → Validation → Toxicity Prediction
Tools: RDKit, Python, pandas, NumPy, scikit-learn, XGBoost, Google Colab
📅 Day 2: ADMET & hERG Liability Prediction
Focus: Predict drug-like properties and major safety liabilities relevant to pharmacokinetics and cardiac safety.
Topics Covered
- ADMET fundamentals and their importance in drug development
- Absorption, distribution, metabolism, excretion and toxicity
- Solubility, permeability, lipophilicity and related molecular properties
- Blood–brain barrier and distribution-related prediction
- Metabolic stability and CYP-related considerations
- Drug-drug interaction and transporter-related safety concepts
- Plasma protein binding and clearance concepts
- Introduction to hERG channel liability and cardiac safety
- hERG inhibition prediction using QSAR and machine learning
- Classification of compounds according to predicted safety risk
- Comparing predicted ADMET and toxicity profiles across compounds
- Interpreting model predictions and uncertainty
🛠️ Hands-on
Generate molecular descriptors, build an ADMET/hERG prediction workflow, classify compounds according to predicted risk, and compare safety profiles.
Workflow:
Molecular Structures → Descriptors → ADMET Prediction → hERG Model → Risk Classification → Safety Profile
Tools: RDKit, PubChem, ChEMBL, Python, scikit-learn, XGBoost, Google Colab
📅 Day 3: Organ-Toxicity Screening, Explainable AI & Compound Prioritisation
Focus: Integrate multiple toxicity endpoints to identify potential organ-specific risks and prioritise compounds for further investigation.
Topics Covered
- Organ-specific toxicity and major safety endpoints
- Hepatotoxicity and liver-injury prediction
- Nephrotoxicity and kidney-related safety assessment
- Neurotoxicity and CNS-related toxicity concepts
- General cytotoxicity and systemic toxicity
- Multi-endpoint toxicity profiling
- Integrating QSAR and ADMET predictions for compound assessment
- Explainable AI for toxicity prediction
- SHAP-based feature importance and interpretation
- Identifying molecular features associated with toxicity
- Multi-parameter safety-risk assessment
- Virtual toxicity screening and compound prioritisation
- Distinguishing computational predictions from experimentally validated toxicity
- Building an interpretable computational safety report
🛠️ Hands-on
Generate organ-toxicity predictions, interpret important molecular features using explainable AI, integrate multiple safety endpoints, and rank compounds according to their predicted risk profile.
Workflow:
Compound Library → Toxicity Prediction → Organ-Specific Screening → SHAP/XAI → Multi-Endpoint Integration → Risk Profiling → Compound Prioritisation
Tools: RDKit, Python, scikit-learn, XGBoost, SHAP, pandas, Plotly, Google Colab
Who Should Enrol?
- Graduate and postgraduate students in Biotechnology, Biochemistry, Chemistry, Pharmacy, Life Sciences, Bioinformatics, and related disciplines.
- PhD scholars and research scholars working in drug discovery, medicinal chemistry, computational toxicology, pharmacology, ADMET, or cheminformatics.
- Academicians and faculty members interested in AI-assisted drug safety and predictive toxicology.
- Industry professionals from pharmaceutical, biotechnology, CRO, drug discovery, toxicology, and computational research sectors.
- Professionals and researchers interested in QSAR, machine learning, ADMET prediction, hERG screening, and organ-toxicity assessment.
Important Dates
Registration Ends
September 30, 2026
IST 4: 30 PM
Workshop Dates
September 30, 2026 – October 2, 2026
IST 5:30 PM
Workshop Outcomes
- Explain the principles of predictive toxicology and QSAR.
- Convert chemical structures into computationally useful molecular representations.
- Generate molecular descriptors and fingerprints using cheminformatics tools.
- Curate and preprocess toxicity datasets for machine learning.
- Build and evaluate QSAR-based toxicity prediction models.
- Interpret classification and regression performance metrics.
- Predict key ADMET properties computationally.
- Understand and model hERG-related cardiac safety liability.
- Screen compounds for potential liver, kidney, CNS and general toxicity risks.
- Apply explainable AI to understand toxicity predictions.
- Identify molecular features associated with predicted toxicity.
- Integrate multiple safety endpoints into a compound risk profile.
- Prioritise compounds for further experimental investigation.
- Understand the limitations, uncertainty and applicability domain of computational toxicity models.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $90
Industry Professional Fee
₹5999 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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