Computer-Aided Drug Design (CADD), Docking, Drug Repurposing, & AI-Driven Pharmacophore Modeling

Master the essentials of Computer-Aided Drug Design (CADD), from target selection to AI-driven pharmacophore modeling. This course provides hands-on training in molecular docking, drug repurposing, and virtual screening to prepare you for real-world drug discovery research.

Core Focus: Structure-based and Ligand-based drug design.
Key Techniques: Molecular Docking, Redocking, and RMSD Validation.
Advanced Analysis: ADMET profiling and AI-driven modeling.

Course Overview

This comprehensive course covers the end-to-end CADD workflow. Participants will learn to navigate biological databases, prepare proteins and ligands, perform high-accuracy docking simulations, and validate results for scientific publication.

What You Will Learn

1. Introduction to Drug Discovery & CADD

Understanding the drug discovery pipeline and the limitations of traditional methods.
Differentiating between structure-based and ligand-based design.
Exploring Drug Repurposing: Advantages over de novo discovery and target selection criteria.

2. Tools, Databases & Preparation

Databases: Hands-on with PDB, PubChem, ChEMBL, and ZINC.
Visualisation: Mastery of PyMOL and Chimera for structural navigation.
Protein Preparation: Learning water removal, charge assignment, and hydrogen addition.
Ligand Preparation: 2D to 3D conversion, energy minimization, and protonation states.

3. Molecular Docking & Validation

Docking Principles: Understanding binding energy and docking scores.
Software: Practical use of AutoDock Vina, SwissDock, CB-Dock, or DockThor.
Scientific Validation: Performing Redocking and calculating RMSD to validate protocols.

4. Advanced Interaction & ADMET Analysis

Interaction Analysis: Visualizing Hydrogen bonding, Hydrophobic, $\pi-\pi$, and electrostatic interactions.
ADMET & Drug-likeness: Applying Lipinski’s Rule of Five and predicting toxicity/absorption parameters.
Virtual Screening: Library preparation and ranking "hits" from large compound sets.
AI-Driven Modeling: Implementing AI-driven pharmacophore modeling.

Mini Project & Scientific Reporting

Students will complete a Mini Project encompassing the full CADD workflow: target selection, docking, validation, and ADMET evaluation. You will also learn to organize computational results and write "Methods, Results, and Discussion" sections for research articles.

Course Outcomes

Perform professional protein and ligand preparation.
Conduct scientifically validated docking and redocking.
Analyze complex protein-ligand interactions.
Execute virtual screening and ADMET filtering.
Prepare results suitable for academic publication.