Computer-Aided Drug Design (CADD) & Machine Learning
Accelerate Drug Discovery with AI: From Docking to Machine Learning Models.
About This Course
Computer-Aided Drug Design has become an indispensable part of modern pharmaceutical research. By simulating molecular interactions, predicting ADMET properties, and screening millions of compounds virtually, CADD significantly speeds up early-stage drug discovery. With the addition of machine learning, researchers can now build data-driven models that enhance prediction accuracy, optimize hit selection, and streamline lead optimization.
This workshop provides a comprehensive introduction to structure-based and ligand-based drug design, molecular docking workflows, scoring functions, QSAR modeling, and ML algorithms used in cheminformatics. Participants will explore real datasets, learn to prepare protein/ligand structures, and perform computational experiments through hands-on dry-lab sessions.
Aim
This workshop aims to equip participants with a deep understanding of computational drug discovery workflows, integrating classical CADD methods with modern machine learning (ML) and AI-based predictive modeling. The goal is to provide hands-on experience with molecular docking, virtual screening, QSAR modeling, and ML-driven drug property prediction. The program prepares learners to utilize computational tools for accelerating drug discovery, reducing experimental costs, and generating high-confidence hit/lead candidates.
Workshop Objectives
- Understand key concepts in structure-based and ligand-based drug design.
- Perform molecular docking, virtual screening, and scoring of ligands.
- Build QSAR and ML models for predicting biological activity & ADMET properties.
- Use cheminformatics tools for dataset preparation and feature extraction.
- Apply end-to-end computational workflows for early-stage drug discovery.
Workshop Structure
Day 1 – CADD Basics & Molecular Representation
- Drug discovery overview: where CADD fits (hit ID, optimization, ADMET).
- Ligand-based vs structure-based design (concept only).
- Molecular representation: SMILES, SDF, basic descriptors/fingerprints.
- Simple CADD workflow: data → descriptors → model/docking → evaluation.
- Hands On: Explore a small set of molecules and their basic properties (conceptual/demo).
Day 2 – Machine Learning for Drug Design
- ML basics: features vs labels; regression vs classification.
- Typical tasks: activity prediction, active vs inactive, simple ADMET flags.
- Simple models (linear/logistic regression, tree-based models – concept).
- Evaluation metrics: accuracy, ROC-AUC, basic regression error.
- Hands On: Outline a small ML pipeline using a toy descriptor + activity table.
Day 3 – Integrated CADD + ML Workflow
- Virtual screening & docking (high-level concept; scoring & ranking).
- Using ML to prioritize compounds and predict basic properties/toxicity.
- End-to-end view: data → descriptors → ML model → filter → docking/experiments.
- Limitations: data quality, overfitting, applicability domain (brief).
- Hands On: Group exercise designing a simple CADD+ML workflow for a hypothetical target.
Who Should Enrol?
- Undergraduate/postgraduate students in Biotechnology, Bioinformatics, Pharmacy, Chemistry, Computational Biology, or related fields.
- Researchers working in drug design, medicinal chemistry, cheminformatics, molecular biology, or wet-lab drug discovery.
- Professionals from pharma, biotech, CROs, and in-silico modeling platforms.
- Beginners with interest in computational drug discovery—no coding experience required.
Important Dates
Registration Ends
01/13/2025
IST 7:00 PM
Workshop Dates
01/13/2026 – 01/15/2026
IST 8:00 PM
Workshop Outcomes
- Gain practical knowledge of protein & ligand preparation for computational analysis.
- Learn to perform molecular docking, pose scoring, and affinity prediction.
- Build ML models for bioactivity and ADMET prediction.
- Work with real chemical datasets for QSAR modeling.
- Understand how CADD integrates with wet-lab drug discovery workflows.
Fee Structure
Student Fee
₹1699 | $60
Ph.D. Scholar / Researcher Fee
₹2699 | $70
Academician / Faculty Fee
₹3699 | $85
Industry Professional Fee
₹4699 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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