Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
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
Representing Molecules for Learning
• Fingerprints, descriptors and learned graph representations
• Data curation: salts, tautomers, duplicates and activity cliffs
• Scaffold and time splits instead of random splits
Representing Sequence and Variation
• Encoding sequence, variants and expression for models
• Population structure and relatedness as leakage sources
• Dimensionality and regularisation in omics-scale feature spaces
Methods Across Both Domains
• Tree ensembles as strong baselines on tabular chemical and genomic features
• Graph neural networks for molecules and interaction networks
• Multi-task and transfer learning across related endpoints
Uncertainty and Applicability
• Applicability domain estimation for chemical models
• Conformal prediction and calibrated uncertainty
• Recognising when a prediction should not be acted on
Chemogenomic Applications
• Target-ligand modelling and proteochemometrics
• Drug response prediction from cell line omics
• Prospective validation and the gap from benchmark to bench
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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