About this course
The -3 Month Advanced CADD Programme is designed for students and professionals seeking practical, research-driven experience in Computer-Aided Drug Design (CADD) and modern drug discovery. This programme combines theoretical concepts with hands-on training in molecular docking, molecular dynamics simulation, virtual screening, QSAR, pharmacophore modeling, bioinformatics, and cheminformatics.
Participants will work on industry-relevant case studies and research projects using leading computational biology tools. The curriculum emphasizes AI-driven drug discovery workflows, protein-ligand interaction analysis, and pharmaceutical research methodologies. Learners gain practical exposure to research data analysis, scientific reporting, and computational drug development.
The programme is ideal for those aspiring to careers in pharmaceutical industries, biotechnology companies, research laboratories, and higher education. Upon successful completion, participants receive a course certificate that strengthens their academic profile and career prospects.
What you will learn
Fundamentals of Computer-Aided Drug Design (CADD)
Drug discovery and drug development workflow
Protein structure preparation
Ligand preparation techniques
Molecular docking using industry-standard software
Structure-based drug design methodologies
Ligand-based drug design concepts
Virtual screening of chemical libraries
Pharmacophore modeling and validation
ADMET prediction and drug-likeness evaluation
Molecular dynamics simulation basics
Protein-ligand interaction analysis
Molecular visualization using PyMOL
Cheminformatics and bioinformatics databases
AI and Machine Learning applications in drug discovery
QSAR modeling fundamentals
Research data interpretation
Scientific report preparation
Industry project execution
Computational drug discovery case studies
Skills you will gain
Docking
Modeling
Screening
Simulation
Cheminformatics
Bioinformatics
Pharmacophore
ADMET
Visualization
Optimization
QSAR
Dynamics
Protein
Ligands
PyMOL
Auto Dock
Discovery
Research
Analytics
AI
Machine Learning
Drug Design
Structural Biology
Validation
Documentation
Course curriculum
3 modules
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T1 = Introduction to Drug Discovery Process
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T2 = Role of Computational Methods
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T3 = Hands-on: Chemical Structure Visualization
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T4 = Biomolecules and Their Properties
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T5 = Structure of Proteins and Ligands
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T6 = Hands-on: Protein Structure Visualization
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T7 = Molecular Visualization Tools
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T8 = Molecular Mechanics and Dynamics Simulations
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T9 = Molecular Mechanics and Dynamics Simulations (continued)
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T10 = Chemical Databases and Data Mining
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T11 = Ligand and Structure-Based Virtual Screening
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T12 = Hands-on: Chemical Data Exploration
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T13 = Advanced Virtual Screening Techniques
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T14 = Virtual Screening using Autodock Vina
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T15 = Principles of Molecular Docking
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T16 = Scoring Functions in Docking
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T17 = Hands-on: Molecular Docking
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T18 = Introduction to Molecular Dynamics
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T19 = Simulation Software (e.g., GROMACS)
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T20 = Hands-on: Analyzing MD Data
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T21 = Chemoinformatics: Data Analysis and Visualization
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T22 = Protein-Ligand Interaction Analysis
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T23 = Hands-on: Protein-Ligand Interaction Analysis
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T24 = Pharmacophore Modeling and Applications
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T25 = Chemoinformatics: Data Analysis and Visualization (continued)
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T26 = Structure-Based Drug Design
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T27 = Ligand-Based Drug Design
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T28 = Hands-on Structure-Based and Ligand-Based Drug Design
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T29 = ADMET in Drug Development
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T30 = Course Conclusion
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T1 = Basics of Machine Learning
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T2 = Supervised, Unsupervised, and Reinforcement Learning
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T3 = Hands-on: Learn the Basics with scikit-learn Library in Python
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T4 = Data Cleaning and Feature Selection
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T5 = Handling Molecular Data
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T6 = Hands-on: Use Pandas and NumPy for Data Preprocessing
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T7 = Regression and Classification Algorithms
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T8 = Deep Learning in Drug Discovery
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T9 = Hands-on: Implement Machine Learning Models using scikit-learn
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T10 = Hands-on: Implement Machine Learning Models using TensorFlow/Keras
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T11 = Predicting Drug-Target Interactions
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T12 = QSAR Modeling
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T13 = Hands-on: Apply Machine Learning to Real Datasets with RDKit
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T14 = Hands-on: Apply Machine Learning to Real Datasets with Cheminformatics
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T15 = Structure-Activity Relationship (SAR) Analysis
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T16 = Hands-on: Use RDKit for SAR Analysis
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T17 = De Novo Drug Design using ML
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T18 = Explore De Novo Design Tools
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T19 = Advanced Machine Learning Techniques in Drug Design
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T20 = Integration of Omics Data in Drug Discovery
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T21 = Clinical Trial Design and Data Analysis
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T22 = Ethical Considerations in Drug Design and Machine Learning
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T23 = Real-World Applications
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T24 = Q&A and Discussion
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T25 = Conclusion
What you need to start
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Basic knowledge of Biology
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Understanding of Biochemistry fundamentals
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Basic Computer skills
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Interest in Drug Discovery
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No prior CADD experience required
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Suitable for beginners and advanced learners
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Passion for research and computational biology
Who this course is for
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Biotechnology students
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Life Science graduates
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Pharmacy (B.Pharm/M.Pharm) students
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Bioinformatics learners
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Microbiology students
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Biochemistry students
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Molecular Biology researchers
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Biomedical Science students
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MSc Life Science students
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Research scholars
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Pharmaceutical professionals
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Career aspirants in Computational Biology
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Students preparing for higher studies
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Anyone interested in AI-driven drug discovery
