AI POWERED VACCINE DEVELOPMENT FOR BIOPHARMA

Week 1: Vaccine Science & AI Foundations

1. Day 1: Vaccine types — live, inactivated, subunit, mRNA, viral vector, and DNA vaccines
2. Day 2: Vaccine development pipeline — discovery, preclinical studies, clinical trials, and approval
3. Day 3: Immunology fundamentals — antigens, antibodies, B/T cells, MHC I/II, and cytokines
4. Day 4: Applications of AI/ML in target identification, epitope prediction, and mRNA design
5. Day 5: AI/ML/DL fundamentals, vaccine databases (IEDB, ViPR, UniProt, PDB), and hands-on epitope data exploration

Week 2: Immunoinformatics & Vaccine Data

1. Day 1: Immunoinformatics, epitope types, MHC binding, immunogenicity, and conservation
2. Day 2: B-cell epitope prediction using BepiPred, ABCpred, and ElliPro
3. Day 3: T-cell epitope prediction using NetMHCpan, MHCflurry, and PickPocket
4. Day 4: Antigenicity, allergenicity, toxicity, and population coverage analysis
5. Day 5: FASTA/VCF data processing and a hands-on epitope prediction and filtering workflow

Week 3: Python & Machine Learning for Vaccine Design

1. Day 1: Biopython — sequence analysis, genome parsing, and ORF translation
2. Day 2: Pandas for epitope datasets and biological feature engineering
3. Day 3: Epitope classification using Random Forest, SVM, and XGBoost
4. Day 4: Imbalanced datasets, class weights, SMOTE, and PR-AUC
5. Day 5: Immunogenicity prediction, clustering, cross-validation, and an ML-based epitope prediction project

Week 4: Deep Learning & Structural Vaccinology

1. Day 1: Protein structures, PDB, AlphaFold DB, and pLDDT confidence scores
2. Day 2: Structural vaccinology and epitope visualization using PyMOL
3. Day 3: CNNs for sequence-based antigenicity prediction
4. Day 4: Protein graphs, GNNs, and protein language models such as ESM-2 and ProtBERT
5. Day 5: Antigen–antibody docking concepts, AlphaFold-based structure prediction, and structural visualization practical

Week 5: mRNA Vaccine Design & Generative AI

1. Day 1: mRNA vaccine structure — 5′ cap, UTRs, ORF, poly-A tail, and codon optimization
2. Day 2: AI-based mRNA optimization and codon design
3. Day 3: RNA secondary structure, minimum free energy, and stability analysis using ViennaRNA
4. Day 4: Lipid nanoparticle delivery prediction and generative AI for antigen design
5. Day 5: ProteinMPNN, antigen stability concepts, and a computational mRNA design project

Week 6: Systems Immunology & Multi-Omics

1. Day 1: RNA 3D structure prediction using tools such as RhoFold
2. Day 2: Molecular dynamics fundamentals and GROMACS for structural stability analysis
3. Day 3: BCR/TCR repertoire data and ML-based immune response prediction
4. Day 4: Single-cell RNA-seq and systems immunology network modeling
5. Day 5: Multimodal AI integrating sequence, structure, HLA, and clinical data; vaccine population-coverage project

Week 7: Clinical Trials & Manufacturing AI

1. Day 1: AI for clinical trial design, patient recruitment, and synthetic control arms
2. Day 2: Digital twins and computational immune-response modeling
3. Day 3: Pharmacovigilance and adverse-event analysis using NLP
4. Day 4: AI for vaccine manufacturing, bioprocess optimization, quality control, and supply chains
5. Day 5: Quality by Design, regulatory validation, and industry case studies involving Moderna and BioNTech

Week 8: Research, Portfolio & Career Development

1. Day 1: Reproducing a vaccine AI research paper, such as DeepVacPred or Vaxign-ML
2. Day 2: Comparative analysis of mRNA, subunit, and viral-vector vaccine design approaches
3. Day 3: Deploying an epitope prediction model using FastAPI and Docker
4. Day 4: Research report writing, technical blogging, and open-source contribution
5. Day 5: Final project presentation, portfolio development, mock interview, and a six-month research roadmap