AI-Powered Drug Discovery with BioPython: Immuno-Chemoinformatics

Course Overview

Program Syllabus

Module 1

About This Course

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.

Module 2

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.

Module 3

Workshop 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.

Module 4

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

Module 5

Who Should Enrol?

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.

Module 6

Important Dates

Module 7

Registration Ends

02/06/2026
IST 07:00 PM

Module 8

Workshop Dates

02/06/2026 – 02/08/2026
IST 08:00 PM

Module 9

Workshop 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.

Module 10

Fee Structure

Module 11

Student Fee

₹1799 | $70

Module 12

Ph.D. Scholar / Researcher Fee

₹2799 | $80

Module 13

Academician / Faculty Fee

₹3799 | $95

Module 14

Industry Professional Fee

₹4799 | $110

Module 15

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Module 16

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 17

Publication Opportunity

Get published in a prestigious open-access journal.

Module 18

Centre of Excellence

Become part of an elite research community.

Module 19

Networking & Learning

Connect with global researchers and mentors.

Module 20

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 21

Need Help?

We’re here for you!

(+91) 120-4781-217

★★★★★

AI-Enabled Nano-Biosensors for Ultra-Sensitive Diagnostics

Very informative workshop

Vaibhavi Meghraj Desai • 11/07/2025 at 6:41 pm

★★★★★

Green Catalysts 2024: Innovating Sustainable Solutions from Biomass to Biofuels

Sir has great knowledge... but he could improve the way of delivering it for more impact.

Sreshtha Satish Jadhav • 07/29/2024 at 6:33 pm

★★★★★

Generative AI and GANs

The mentor was supportive, clear in their guidance, and encouraged active participation throughout the process.

António Ricardo de Bastos Teixeira • 07/03/2025 at 10:02 pm

★★★★★

AI and Ethics: Governance and Regulation

Good but less innovative

Saraswathi Sivamani • 01/06/2025 at 11:23 am

View All Feedbacks →

Course Overview

Program Syllabus

Module 1

About This Course

Module 2

Aim

Module 3

Workshop 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.

Module 4

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

Module 5

Who Should Enrol?

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.

Module 6

Important Dates

Module 7

Registration Ends

02/06/2026
IST 07:00 PM

Module 8

Workshop Dates

02/06/2026 – 02/08/2026
IST 08:00 PM

Module 9

Workshop 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.

Module 10

Fee Structure

Module 11

Student Fee

₹1799 | $70

Module 12

Ph.D. Scholar / Researcher Fee

₹2799 | $80

Module 13

Academician / Faculty Fee

₹3799 | $95

Module 14

Industry Professional Fee

₹4799 | $110

Module 15

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Module 16

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 17

Publication Opportunity

Get published in a prestigious open-access journal.

Module 18

Centre of Excellence

Become part of an elite research community.

Module 19

Networking & Learning

Connect with global researchers and mentors.

Module 20

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 21

Need Help?

We’re here for you!

(+91) 120-4781-217

★★★★★

AI-Enabled Nano-Biosensors for Ultra-Sensitive Diagnostics

Very informative workshop

Vaibhavi Meghraj Desai • 11/07/2025 at 6:41 pm

★★★★★

Green Catalysts 2024: Innovating Sustainable Solutions from Biomass to Biofuels

Sir has great knowledge... but he could improve the way of delivering it for more impact.

Sreshtha Satish Jadhav • 07/29/2024 at 6:33 pm

★★★★★

Generative AI and GANs

The mentor was supportive, clear in their guidance, and encouraged active participation throughout the process.

António Ricardo de Bastos Teixeira • 07/03/2025 at 10:02 pm

★★★★★

AI and Ethics: Governance and Regulation

Good but less innovative

Saraswathi Sivamani • 01/06/2025 at 11:23 am

View All Feedbacks →

Enrol Now
Explore

AI-Powered Drug Discovery with BioPython: Immuno-Chemoinformatics

Course Overview

Program Syllabus

About This Course

Aim

Workshop Objectives

Day 1: Biopython for Drug Discovery &amp; Immunoinformatics Basics

Who Should Enrol?

Important Dates

Registration Ends

Workshop Dates

Workshop Outcomes

Fee Structure

Student Fee

Ph.D. Scholar / Researcher Fee

Academician / Faculty Fee

Industry Professional Fee

What You&#8217;ll Gain

Join Our Hall of Fame!

Publication Opportunity

Centre of Excellence

Networking &#038; Learning

Global Recognition

Need Help?

Lead Instructor

Dr. Sarah Chen