AI-Powered Drug Discovery: Molecular Screening, ADMET Prediction, QSAR Modeling & Lead Optimization
Accelerate Drug Discovery with AI—from Molecular Screening to Lead Prediction.
Skills you will gain:
About Program:
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:
Program 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
Career Supporting Skills
Program 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.