About this course
The 6-Month Advanced CADD Programme — A Research-Oriented Course for Industry-Ready Design Professionals is designed for students, researchers, and professionals seeking advanced practical expertise in Computer-Aided Drug Design (CADD) and modern computational drug discovery. This research-oriented programme integrates theoretical foundations with extensive hands-on training in molecular docking, molecular dynamics simulations, virtual screening, QSAR, pharmacophore modeling, bioinformatics, cheminformatics, and structure-based drug design.
Participants will work on real-world research projects, industry-relevant case studies, and computational drug discovery workflows using leading bioinformatics and CADD tools. The curriculum emphasizes AI-driven drug discovery, protein–ligand interaction analysis, molecular modeling, lead identification and optimization, and pharmaceutical research methodologies.
Throughout the programme, learners gain practical experience in research data analysis, computational workflow development, scientific literature analysis, result interpretation, scientific reporting, and research project execution. The extended six-month duration provides greater scope for developing research-oriented problem-solving skills and industry-ready computational drug design expertise.
This programme is ideal for those aspiring to build careers in pharmaceutical companies, biotechnology organizations, computational biology teams, research laboratories, drug discovery organizations, and higher education/research. Upon successful completion, participants receive a programme completion certificate, supporting their academic profile, research experience, and professional career development.
What you will learn
Fundamentals of Computer-Aided Drug Design (CADD).
Drug discovery and drug development workflow.
Protein structure preparation and validation.
Ligand preparation and optimization.
Molecular docking using industry-standard software.
Virtual screening of chemical libraries.
Molecular dynamics simulation techniques.
Protein-ligand interaction analysis.
Pharmacophore modeling and QSAR studies.
ADMET prediction and drug-likeness evaluation.
Bioinformatics databases and sequence analysis.
Cheminformatics concepts and molecular descriptors.
AI applications in computational drug discovery.
Scientific report writing and research documentation.
Best practices in pharmaceutical and biotechnology research.
Skills you will gain
Bioinformatics
Cheminformatics
Docking
Simulation
QSAR
Pharmacophore
ADMET
Screening
Visualization
Python
Linux
Databases
Genomics
Proteomics
Drug Discovery
Molecular Modeling
AI
Machine Learning
Research
Documentation
Analysis
Statistics
Problem Solving
Communication
Course curriculum
7 modules
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M1T1 = Introduction to Bioinformatics
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M1T2 = NCBI Database Overview
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M1T3 = Genbank Database Practical Exercises
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M1T4 = UCSC Genome Browser Overview
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M1T5 = UCSC Genome Browser Hands-on Exercises
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M1T6 = Pubmed Database Introduction
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M1T7 = Clinvar Database Overview
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M1T8 = KEGG Database Overview and Exercises
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M1T9 = Protein Databases (UniProt)
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M1T10 = Protein Databases (PDB)
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M1T11 = Online BLAST Introduction and Exercises
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M1T12 = Standalone BLAST Setup and Exercises
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M1T13 = Standalone BLAST Advanced Exercises
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M1T14 = Multiple Sequence Alignment with ClustalW
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M1T15 = Multiple Sequence Alignment with MEGA
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M2T1 = Overview and Installation of Linux
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M2T2 = Basic Linux Commands
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M2T3 = Advanced Linux Commands
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M2T4 = Package Management using Repository
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M2T5 = Package Management using Source Code
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M3T1 = Introduction to Python
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M3T2= Data Types
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M3T3= String Handling
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M3T4= Data Structure
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M3T5=Control Structure
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M3T6 = Function
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M3T7= File Handling
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M3T8= Data Manipulation
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M3T9= Data Visualization
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M3T10= Biopython
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M4T1 = Introduction and Installation of R
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M4T2= Data Types in R
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M4T3= Data Structure
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M4T4= File Handling
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M4T5=Control Structure
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M4T6 = Function
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M4T7= Package Management
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M4T8= Data Manipulation
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M4T9= Data Visualization
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M4T10= Statistical Analysis
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M6T1 =Introduction to Drug Discovery Process
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M6T2=Role of Computational Methods
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M6T3= Hands-on: Chemical Structure Visualization
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M6T4= Biomolecules and Their Properties
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M6T5= Structure of Proteins and Ligands
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M6T6= Hands-on: Protein Structure Visualization
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M7T7= Molecular Visualization Tools
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M6T8= Molecular Mechanics and Dynamics Simulations
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M6T9= Molecular Mechanics and Dynamics Simulations (continued)
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M6T10= Chemical Databases and Data Mining
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M6T11= Ligand and Structure-Based Virtual Screening
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M6T12= Hands-on: Chemical Data Exploration
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M6T13= Advanced Virtual Screening Techniques
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M6T14= Virtual Screening using Autodock Vina
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M6T15= Principles of Molecular Docking
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M6T16= Scoring Functions in Docking
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M6T17= Hands-on: Molecular Docking
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M6T18= Introduction to Molecular Dynamics
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M6T19= Simulation Software (e.g., GROMACS)
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M6T20= Hands-on: Analyzing MD Data
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M6T21= Chemoinformatics: Data Analysis and Visualization
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M6T22= Protein-Ligand Interaction Analysis
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M6T23= Hands-on Protein-Ligand Interaction Analysis
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M6T24= Pharmacophore Modeling and Applications
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M6T25= Chemoinformatics: Data Analysis and Visualization (continued)
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M6T26= Structure-Based Drug Design
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M6T27= Ligand-Based Drug Design
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M6T28= Hands-on Structure-Based and Ligand-Based Drug Design
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M6T29= ADMET in Drug Development
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M6T30= Course Conclusion
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M7T1= Basics of Machine Learning
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M7T2= Supervised, Unsupervised, and Reinforcement Learning
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M7T3= Hands-on: Learn the Basics with scikit-learn Library in Python
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M7T4= Data Cleaning and Feature Selection
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M7T5= Handling Molecular Data
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M7T6= Hands-on: Use Pandas and NumPy for Data Preprocessing
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M7T7= Regression and Classification Algorithms
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M7T8= Deep Learning in Drug Discovery
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M7T9= Hands-on: Implement Machine Learning Models using scikit-learn
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M7T10= Hands-on: Implement Machine Learning Models using TensorFlow/Keras
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M7T11= Predicting Drug-Target Interactions
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M7T12= QSAR Modeling
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M7T13= Hands-on: Apply Machine Learning to Real Datasets with RDKit
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M7T14= Hands-on: Apply Machine Learning to Real Datasets with Cheminformatics
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M7T15= Structure-Activity Relationship (SAR) Analysis
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M7T16= Hands-on: Use RDKit for SAR Analysis
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M7T17= De Novo Drug Design using ML
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M7T18= Explore De Novo Design Tools
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M7T19= Advanced Machine Learning Techniques in Drug Design
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M7T20= Integration of Omics Data in Drug Discovery
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M7T21= Clinical Trial Design and Data Analysis
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M7T22= Ethical Considerations in Drug Design and Machine Learning
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M7T23= Real-World Applications
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M7T24= Q&A and discussion
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M7T25= Conclusion
What you need to start
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Basic understanding of biology or life sciences
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Fundamental knowledge of biochemistry is beneficial
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Interest in computational biology and drug discovery
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Basic computer skills
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No prior CADD experience required
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Programming knowledge is optional but helpful
Who this course is for
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B.Sc. Life Sciences students
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M.Sc. Life Sciences students
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Biotechnology students
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Biochemistry students
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Microbiology students
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Pharmacy (B.Pharm & M.Pharm) students
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Pharmaceutical Sciences students
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Bioinformatics learners
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Research scholars
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Fresh graduates
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Academic researchers
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Professionals transitioning into computational drug discovery
