Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Immunoinformatics and Vaccine Development sits at the intersection of computational biology and immunology, a field accelerated dramatically by recent AI advances. You learn the immunology foundations — antigens, epitopes and the immune response — then the computational methods that predict them: B- and T-cell epitope prediction, MHC-binding models and antigen analysis. The course covers modern AI-driven approaches to reverse vaccinology and in-silico vaccine design, and how they compress timelines that once took years. You finish able to reason through a computational vaccine-design workflow. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI and immunoinformatics to vaccine development — epitope prediction, antigen and immune-response modelling, and computational design of vaccine candidates.
📋 Course Objectives
1. Explain antigens, epitopes and the immune response.
2. Predict B-cell and T-cell epitopes computationally.
3. Model MHC binding and antigen properties.
4. Apply AI to reverse vaccinology and candidate design.
5. Interpret an in-silico vaccine-design workflow.
👥 Who Should Enroll?
• Immunology and vaccinology researchers
• Bioinformatics and computational-biology scientists
• Biotech and pharma R&D professionals
• Students specialising in immunoinformatics
🚀 Key Learning Outcomes
• An understanding of AI-driven vaccine design.
• The ability to reason through immunoinformatics workflows.
• A foundation for computational immunology research.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Antigen Processing and Presentation
• MHC class I and II pathways and the constraints they impose on epitopes
• HLA polymorphism and population coverage as a design requirement
• B-cell versus T-cell epitope distinctions that drive different methods
Epitope and Binding Prediction
• MHC binding prediction with NetMHCpan-class tools and their training bias
• Immunogenicity versus binding: why predicted binders often fail
• B-cell epitope prediction and its comparatively poor performance
Constructing Vaccine Candidates
• Multi-epitope construct design, linkers and adjuvant selection
• Population coverage analysis across HLA distributions
• Structural validation and conformational epitope preservation
Cross-Reactivity and Risk
• Autoimmunity screening against the human proteome
• Allergenicity and toxicity prediction
• Antibody-dependent enhancement as a design consideration
From In Silico to Trial
• Experimental validation cascade and its expected attrition
• Manufacturing and platform choice, including mRNA and subunit routes
• Regulatory expectations for computationally designed candidates
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | NCBI Tools |
| Covered Tool / Platform | BLAST |
| Covered Tool / Platform | PyMOL |
| Covered Tool / Platform | AutoDock |
| Covered Tool / Platform | ChemDraw |
| Covered Tool / Platform | Clustal Omega |
| Covered Tool / Platform | Python |
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