AI-Powered Drug Discovery with BioPython: Immuno-Chemoinformatics
Build AI Pipelines that Connect Immune Biology and Chemical Space
Skills you will gain:
About Program:
Immuno-chemoinformatics combines immunology + bioinformatics + chemoinformatics to accelerate the discovery of immune-targeted therapeutics such as vaccines, antibodies, immune modulators, and small molecules acting on immune pathways. Modern discovery increasingly relies on data-driven approaches—epitope prediction, antigen characterization, immunogenicity signals, toxicity screening, and molecular similarity—supported by open databases and computational tools. With growth in immunotherapy and vaccine R&D, professionals who can integrate biological and chemical data are in high demand.
This workshop provides a hands-on, dry-lab learning pathway using BioPython for sequence handling and biological feature extraction, along with Python-based analytics for building AI models. Participants will explore how to collect and clean datasets from public resources, generate sequence-derived and chemistry-derived features, and train ML models for tasks such as immunogenicity classification, epitope prioritization, and candidate ranking. Practical sessions will emphasize model evaluation, explainability, and decision-making for discovery pipelines.
Aim: This workshop aims to train participants in building AI-enabled drug discovery workflows using BioPython and core machine learning tools for immuno-chemoinformatics. It focuses on how biological sequence data (antigens, antibodies, epitopes) can be integrated with chemical descriptors to support screening, prioritization, and early-stage discovery. Participants will learn reproducible pipelines for data retrieval, feature extraction, predictive modeling, and result interpretation. The program bridges immunology, bioinformatics, and AI-driven medicinal discovery.
Program Objectives:
- Use BioPython to retrieve, parse, and analyze biological sequences and annotations.
- Understand immuno-chemoinformatics workflows for therapeutic discovery.
- Build features from sequences (k-mers, motifs, composition) and molecules (fingerprints/descriptors).
- Train ML models for prediction and prioritization (classification/ranking).
- Evaluate and interpret models with metrics and explainability methods.
What you will learn?
Day 1: Biopython for Drug Discovery & Immunoinformatics Basics
- Python setup for bio and chem workflows (Colab/Jupyter), data formats (FASTA, PDB, SDF/SMILES, CSV)
- Biopython essentials: sequence I/O, translation, alignment basics, protein properties, motif scanning
- Mapping drug-target context: proteins, epitopes, antigens, receptors (concept + examples)
- Immunoinformatics fundamentals: T-cell/B-cell epitopes, HLA binding concepts, antigenicity/toxicity/allergenicity overview
- Data preprocessing: cleaning sequence datasets, balancing labels, handling missing/ambiguous residues
- Feature engineering for sequences: k-mers, physicochemical descriptors, embeddings overview
- Tools: Python, Biopython, Pandas, NumPy, Matplotlib, Scikit-learn, Jupyter/Colab
Day 2: Immuno Chemoinformatics Modeling From Features to Predictors
- Immuno-AI models: HLA binding prediction workflow (classification/regression framing)
- Supervised ML for bioscience: logistic regression, random forests, SVM; when to use what
- Model evaluation: cross-validation, ROC-AUC, PR-AUC, F1, calibration basics, leakage checks
- Chemoinformatics essentials: SMILES, molecular standardization, fingerprints (Morgan), descriptors
- QSAR pipeline: feature generation → model training → interpretation (importance, SHAP overview)
- Linking immuno + chemical views: prioritizing peptides/small molecules with multi-criteria scoring
- Tools: RDKit, Scikit-learn, Seaborn/Matplotlib, (optional) SHAP
Day 3: Advanced AI Research-Grade Reporting & Mini-Deployment
- Deep learning overview for sequences and molecules: MLP/CNN basics, embeddings, transfer learning ideas
- NLP for bio/drug discovery: mining abstracts for targets, disease keywords, and mechanism hints (intro workflow)
- Hyperparameter tuning & comparison: GridSearchCV/RandomizedSearchCV, model selection best practices
- Hands-on case study:Build an epitope/HLA binding classifier or a QSAR virtual screening model
- Generate a final ranked shortlist + metrics + interpretation
- Lightweight deployment demo: saving model, inference pipeline, simple UI (optional)
- Tools: TensorFlow/Keras or PyTorch (intro), Scikit-learn tuning, Jupyter/Colab, (optional) Streamlit
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills
Program Outcomes
Participants will be able to:
- Build end-to-end pipelines combining immune sequence data with molecular features.
- Use BioPython for sequence parsing, annotation, and feature extraction.
- Train ML models for immunogenicity/epitope prioritization and candidate screening.
- Evaluate model performance and interpret predictions for decision support.
- Produce reproducible notebooks suitable for research projects or portfolios.