AI Solutions for Biopharma & Industrial Biotech

Week 1: Biotech Pipeline & Data

1. Day 1: Industrial biotechnology — enzymes, fermentation and bioplastics
2. Day 2: Bioprocess fundamentals — upstream/downstream processing, PAT and QbD
3. Day 3: Databases — BRENDA, KEGG Enzyme and UniProt
4. Day 4: Enzyme kinetics — Michaelis–Menten, Km, Vmax and ML concepts
5. Day 5: Metabolic pathways, flux balance analysis and enzyme-data exploration

Week 2: Python & Cheminformatics

1. Day 1: RDKit, SMILES and SMARTS
2. Day 2: Molecular descriptors and Morgan fingerprints
3. Day 3: Biopython for enzyme sequences and mutations
4. Day 4: Pandas for fermentation data analysis
5. Day 5: DOE, ANOVA and enzyme–substrate–yield dataset development

Week 3: Machine Learning for Enzyme Engineering

1. Day 1: Enzyme activity prediction using Random Forest and XGBoost
2. Day 2: QSAR-style modeling for enzyme stability and pH tolerance
3. Day 3: Feature engineering — amino acid properties and PSSM
4. Day 4: Active learning and multi-objective optimization
5. Day 5: Thermostability prediction and enzyme activity mini-project

Week 4: Deep Learning & Structural Biology

1. Day 1: Protein structures using PDB and AlphaFold DB
2. Day 2: PyMOL for active-site visualization and mutation mapping
3. Day 3: Deep learning for catalytic-site prediction
4. Day 4: ESM-2 embeddings and graph neural networks
5. Day 5: Enzyme–substrate docking using AutoDock Vina

Week 5: Generative AI & Protein Design

1. Day 1: ProteinMPNN and inverse folding
2. Day 2: RFdiffusion and protein scaffold design
3. Day 3: Constrained protein design and active-site preservation
4. Day 4: Rosetta energy scoring and candidate ranking
5. Day 5: GROMACS molecular dynamics and enzyme stability mini-project

Week 6: AI for Bioprocess Optimization

1. Day 1: Bioreactor sensor data and time-series analysis
2. Day 2: Anomaly detection for contamination risks and batch failures
3. Day 3: Reinforcement learning for feed-rate optimization
4. Day 4: Computer vision and ML for downstream purification
5. Day 5: Digital twins and bioreactor monitoring dashboard

Week 7: Biofuels, Bioplastics & Sustainability

1. Day 1: ML in metabolic engineering and strain design
2. Day 2: Retrosynthesis for bio-based chemicals
3. Day 3: Life-cycle assessment and carbon-footprint prediction
4. Day 4: Enzyme cocktail optimization and bioplastic property prediction
5. Day 5: Scale-up modeling and PET-degrading enzyme mini-project

Week 8: Industry & Capstone Project

1. Day 1: MLOps, regulatory considerations and model monitoring
2. Day 2: Intellectual property, patent searching and industry case studies
3. Day 3: Capstone planning — select an enzyme or industrial process
4. Day 4: Develop the AI workflow, evaluate candidates and assess feasibility
5. Day 5: Final presentation, GitHub portfolio and mock interview