Computer-Aided Drug Design

About the Course

The Computer-Aided Drug Design (CADD) course by SkillDzire provides an in-depth understanding of how computational tools are revolutionizing modern drug discovery. Students will gain expertise in molecular modeling, docking, pharmacophore design, QSAR analysis, and molecular dynamics. The course also introduces emerging AI-driven methodologies for de novo drug design and ADMET prediction, preparing learners for industry and research applications in pharma and biotech.

Comprehensive training on molecular modeling & simulation workflows

Hands-on projects with industry-standard software and datasets

Exposure to AI/ML applications in computational chemistry

Real-world case studies with disease-specific modeling tasks

Curriculum

Introduction & Workflow

Introduction to CADD & Drug Discovery Workflow
Role of CADD in Industry
CADD Workflow & Applications

Biological Databases

Overview of Major Databases (PDB, UniProt, ChEMBL, etc.)
Retrieving and Preparing Biological Data
Target Identification Using Databases

Molecular Modeling

2D to 3D Structure Generation
Energy Minimization & Force Fields
Homology Modeling Basics

Molecular Visualization

Using PyMOL/Chimera for Structure Viewing
Interpreting Protein-Ligand Interactions

Ligand & Protein Preparation

Ligand Optimization
Protein Cleaning (Removing Water/Ions, Adding Hydrogens)
Protonation, Charge Assignment

Molecular Docking

Principles of Docking: Rigid vs Flexible
Scoring Functions & Docking Algorithms
Performing Docking Using AutoDock or Similar Tools

Pharmacophore Modeling

Concept of Pharmacophores & Feature Identification
Structure-Based & Ligand-Based Modeling
Tool Use: PharmaGist, LigandScout

QSAR & ADMET Prediction

Introduction to QSAR and Molecular Descriptors
QSAR Model Development
ADMET Tools (SwissADME, pkCSM, etc.)

Molecular Dynamics (MD) Simulations

Introduction to MD and Energy Terms
Running MD Simulations with GROMACS/Desmond
Post-simulation Analysis

Chemoinformatics

Clustering, Virtual Screening Concepts
Tools (RDKit, KNIME, etc.)

AI/ML in Drug Design

Introduction to ML Algorithms for Drug Design
Feature Selection, Training & Validation
Implementing ML with Molecular Data

Disease-Specific Projects

Designing Workflow for a Specific Disease
Step-by-step Execution (Target to Prediction)

Hands-On Real-Time Exposure Tasks

Molecular Dynamics Simulation of Protein–Ligand Complexes with Enhanced Sampling (Metadynamics, Accelerated MD)
AI/ML-Driven De Novo Drug Design Using Generative Models (GANs, Transformers)
ADMET & Toxicity Filtering with Deep Learning (hERG Inhibition, BBB Penetration, CYP450 Prediction)
Structure-Based Design of Multi-Target Inhibitors for Antimicrobial Resistance (AMR)
Ligand-Based & Pharmacophore Modeling Integrated with High-Throughput Virtual Screening
Cryo-EM Integrated CADD for Large Protein Complexes
Molecular Docking & Free Energy Perturbation (FEP+) for Accurate Binding Affinity Prediction
Network Pharmacology & Polypharmacology: Designing Drugs for Complex Diseases (Cancer, Neurodegeneration)
Quantum Mechanics/Molecular Mechanics (QM/MM) Hybrid Approaches for Enzyme Inhibitors
CADD for RNA & Protein–Protein Interaction (PPI) Targets

Download Full Curriculum (PDF)

Projects You Will Work On

Molecular Docking & MD Simulation

Perform protein-ligand docking and simulate complex stability using GROMACS or Desmond.

AI/ML-Driven De Novo Drug Design

Use GANs or Transformers to design novel molecular scaffolds and predict binding affinities.

ADMET Prediction & Toxicity Filtering

Develop machine learning models for drug-likeness, BBB permeability, and hERG inhibition prediction.

Computer-Aided Drug Design (CADD)

Learning Mode

Software Tools

AI Integration

Career Support