Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
From Molecules to Meaning: QSAR Modeling with ML and Orange3. This intensive program equips participants with the skills to build predictive Quantitative Structure-Activity Relationship (QSAR) models using machine learning techniques. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
From Molecules to Meaning: QSAR Modeling with ML and Orange3. This intensive program equips participants with the skills to build predictive Quantitative Structure-Activity Relationship (QSAR) models using machine learning techniques.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of QSAR and Molecular Descriptors
Understand the principles of Quantitative Structure-Activity Relationship modeling and its significance in modern drug discovery • Explore diverse physicochemical descriptors that encode molecular structure into computable features • Analyze descriptor relevance and selection strategies for optimal model performance
Orange3 Platform and Data Workflow
Navigate the Orange3 visual programming interface for interactive data science and machine learning • Import, clean, and preprocess chemical datasets for QSAR model development • Construct automated data pipelines connecting descriptor generation to model training workflows
Supervised Machine Learning Algorithms
Implement Random Forest algorithms for robust prediction of biological activity from molecular features • Apply Support Vector Machine (SVM) models to capture complex non-linear structure-activity relationships • Compare algorithm performance characteristics and select optimal methods for specific datasets
Model Validation and Robustness Assessment
Execute leave-one-out (LOO) validation to assess model predictivity on individual compounds • Design random sampling and k-fold cross-validation protocols for reliable performance estimation • Evaluate statistical metrics including R², Q², RMSE, and external test set predictions
Results Interpretation and Visualization
Interpret model outputs to identify structural features driving biological activity • Generate publication-quality visualizations including scatter plots, regression lines, and feature importance charts • Communicate QSAR findings effectively to interdisciplinary stakeholders and decision-makers
Pharmaceutical Applications and Case Studies
Apply validated QSAR models to prioritize compounds in virtual screening campaigns • Examine real-world case studies demonstrating ML-driven QSAR in lead optimization • Integrate predictive modeling into contemporary pharmaceutical development pipelines
Advanced Topics and Emerging Trends
Investigate deep learning approaches and ensemble methods for enhanced QSAR prediction accuracy • Address challenges of model applicability domain and extrapolation beyond training data • Explore regulatory perspectives on QSAR models for toxicity and environmental fate prediction
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Orange3 |
| Covered Tool / Platform | Random Forest |
| Covered Tool / Platform | SVM |
| Covered Tool / Platform | Python ecosystem |
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