AI-Powered Drug Discovery: Molecular Screening, ADMET and Lead Prediction
Accelerate Drug Discovery with AI—from Molecular Screening to Lead Prediction.
About This Course
This workshop introduces participants to the use of artificial intelligence in accelerating modern drug discovery. It covers AI-powered molecular screening, prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, and data-driven identification of promising lead compounds. Through expert-led explanations and hands-on activities, participants will learn how machine learning and computational tools can help prioritize molecules, reduce experimental costs, minimize late-stage failures, and support faster, more informed decisions in pharmaceutical and biomedical research.
Aim
Workshop Objectives
- Understand the role of artificial intelligence and machine learning in modern drug discovery.
- Explore computational approaches for molecular screening and compound prioritization.
- Learn how to predict key ADMET properties of potential drug candidates.
- Apply AI-based techniques to identify and rank promising lead compounds.
- Interpret molecular datasets and predictive model results effectively.
- Understand how AI can reduce drug development time, costs, and late-stage failures.
- Gain practical exposure to data-driven drug discovery workflows and relevant computational tools.
Workshop Structure
📅 Day 1: AI-Driven Molecular Screening and Compound Prioritization
- Drug discovery pipeline: target identification to lead optimization
- Role of AI and machine learning in early-stage drug discovery
- Molecular representations: SMILES, molecular descriptors, and fingerprints
- Drug-likeness assessment using physicochemical properties
- Lipinski’s Rule of Five and compound filtering
- Virtual screening and molecular-similarity analysis
- Ranking compounds for further investigation
🛠️ Hands-on: Build a molecular-screening pipeline in Google Colab to retrieve compounds, calculate molecular descriptors, apply drug-likeness filters, and rank candidate molecules.
🧰 Tools Covered: Google Colab, Python, RDKit, Pandas, and PubChem PUG REST API
📅 Day 2: Machine Learning for ADMET Prediction
- Understanding absorption, distribution, metabolism, excretion, and toxicity
- Importance of ADMET evaluation in reducing late-stage drug failure
- Preparing molecular datasets for predictive modelling
- Descriptor and fingerprint-based feature engineering
- QSAR and machine-learning approaches for ADMET prediction
- Model training using Random Forest and classification algorithms
- Performance evaluation using ROC-AUC, accuracy, precision, and recall
- Interpreting ADMET predictions and identifying risk alerts
🛠️ Hands-on: Train and evaluate a machine-learning model in Google Colab to predict an ADMET-related property and classify molecules as favourable or high-risk candidates.
🧰 Tools Covered: Google Colab, RDKit, Scikit-learn, Pandas, and Matplotlib
📅 Day 3: Lead Prediction, Explainable AI and Multi-Parameter Optimization
- Lead identification and compound-ranking strategies
- Integrating activity, drug-likeness, and ADMET predictions
- Multi-parameter optimization for lead selection
- AI-based scoring and ranking of candidate compounds
- Explainable AI for understanding molecular predictions
- Introduction to graph neural networks and generative AI in drug design
- Model limitations, data quality, applicability domains, and validation
- Designing an end-to-end AI-powered drug-discovery workflow
🛠️ Hands-on: Develop a lead-prioritization notebook that combines predicted activity, drug-likeness, and ADMET scores to rank compounds and visualize the strongest candidates.
🧰 Tools Covered: Google Colab, Python, RDKit, Scikit-learn, SHAP, and Seaborn
Important Dates
Registration Ends
September 28, 2026
IST 4.30 pm IST
Workshop Dates
September 28, 2026 – September 30, 2026
IST 5:30 PM IST
Workshop Outcomes
- Explain the role of AI and machine learning in drug discovery.
- Perform basic molecular screening and compound prioritization.
- Evaluate key ADMET properties of potential drug candidates.
- Apply predictive models to identify promising lead compounds.
- Interpret molecular data and AI-generated prediction results.
- Recognize compounds with potential safety or efficacy concerns.
- Develop a structured, data-driven workflow for early-stage drug discovery.
Fee Structure
Student
₹2999 | $80
Ph.D. Scholar / Researcher
₹3999 | $90
Academician / Faculty
₹4999 | $105
Industry Professional
₹6999 | $120
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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