Hands-On | Machine Learning for Drug Discovery & Genomics
Build AI Models for Drugs & Genomes—Hands-On Learning for Future Scientists
About This Course
Machine learning is transforming both drug discovery and genomics by enabling faster identification of drug targets, prediction of molecular interactions, and analysis of complex genomic datasets. Traditional experimental approaches are time-consuming and costly, whereas ML models can analyze vast datasets to uncover hidden biological patterns and accelerate decision-making in research and development.
This workshop provides a hands-on, dry-lab experience where participants will work with real datasets to build ML models for tasks such as drug-target prediction, gene expression analysis, and disease classification. Using Python-based tools and libraries, participants will learn end-to-end workflows—from data preprocessing to model evaluation—preparing them for cutting-edge roles in AI-driven life sciences.
Aim
This workshop aims to provide hands-on experience in applying machine learning techniques to drug discovery and genomics. It focuses on building predictive models for target identification, drug response, and genomic data analysis. Participants will learn how to integrate biological datasets with ML workflows for real-world applications. The program bridges AI, bioinformatics, and pharmaceutical research.
Workshop Objectives
- Understand ML fundamentals for drug discovery and genomics.
- Learn data preprocessing and feature engineering for biological datasets.
- Build predictive models for drug-target interaction and disease classification.
- Evaluate model performance using relevant biomedical metrics.
- Apply ML workflows to real-world drug discovery and genomics problems.
Workshop Structure
Day 1: Genomics Data Processing & ML Foundations
- Retrieval and preprocessing of genomic datasets from NCBI GEO / ENA (FASTQ/FASTA formats)
- Quality control and trimming using FastQC and preprocessing pipelines
- Feature extraction from sequences (k-mers, GC content, embeddings)
- Encoding biological sequences using one-hot, k-mer vectors, and transformer embeddings
- Dimensionality reduction using PCA / t-SNE / UMAP on genomic features
- Classification of gene sequences using SVM / Random Forest models
- Variant analysis and SNP classification using ML pipelines
- Building a basic genomic prediction model (disease vs normal classification)
Day 2: Machine Learning in Drug Discovery
- Retrieval and curation of chemical datasets from PubChem / ChEMBL
- Molecular descriptor calculation using RDKit (physicochemical properties)
- Conversion of molecules into fingerprints (ECFP, MACCS)
- QSAR modeling using regression/classification ML models
- Drug-target interaction prediction using matrix factorization / ML models
- Training ML models for binding affinity prediction (regression tasks)
- Virtual screening using ML-based filtering of compound libraries
- Model evaluation using ROC-AUC, precision-recall, RMSE metrics
Day 3: Integrated AI Pipelines & Advanced Applications
- Integration of genomic & chemical data for target identification
- Multi-omics data fusion using ML-based feature integration techniques
- Building a drug response prediction model using genomic signatures
- Implementation of deep learning models (CNNs for sequence analysis)
- Training a Graph Neural Network (GNN) for molecular property prediction
- Explainability using SHAP / feature importance in biological models
- End-to-end pipeline: target identification → lead prediction → validation
- Deployment of ML models using Streamlit / API for real-time predictions
Important Dates
Registration Ends
04/07/2026
IST 7:00 PM
Workshop Dates
04/07/2026 – 04/09/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Build ML models for drug discovery and genomics applications.
- Analyze genomic datasets and predict biological outcomes.
- Apply feature engineering and model evaluation techniques.
- Develop reproducible ML pipelines for biomedical research.
- Translate AI insights into practical research and clinical applications.
Fee Structure
Student Fee
₹2499 | $60
Ph.D. Scholar / Researcher Fee
₹3499 | $70
Academician / Faculty Fee
₹4499 | $80
Industry Professional Fee
₹5499 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →