Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for De Novo Drug Design explores one of the most exciting frontiers in computational chemistry: using generative AI to invent entirely new molecules rather than screen existing ones. You learn how molecules are represented for machine learning, and the generative approaches — from variational and recurrent models to reinforcement learning and diffusion — that design candidates with target properties. The course covers property optimisation, synthesisability, and validating generated molecules, connecting the methods to real drug-discovery goals. You finish able to reason about a generative molecular-design workflow. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI for de novo drug design — using generative models to design novel molecules with desired properties for drug discovery.
📋 Course Objectives
1. Represent molecules for machine learning.
2. Apply generative models to molecule design.
3. Optimise generated molecules for target properties.
4. Assess synthesisability and validity.
5. Connect generation to drug-discovery goals.
👥 Who Should Enroll?
• Medicinal and computational chemists
• Cheminformatics and drug-discovery scientists
• ML researchers in chemistry
• Students of computational chemistry
🚀 Key Learning Outcomes
• An understanding of generative molecular design.
• The ability to reason about de novo workflows.
• A foundation in AI-driven chemistry.
• 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
Module 1 — Strategic Foundations
Domain context and core principles of De Novo design • Hands-on environment setup for chemistry AI • Milestone review: assumptions, risks, and quality checkpoints
Module 2 — Data Engineering & Feature Intelligence
Workflow design for traceability and reproducibility • Implementation lab: optimizing design under practical constraints • Quality validation cycles and remediation steps
Module 3 — Advanced Modeling & Optimization
Comparative architecture decision analysis • Experiment strategy for AI under real-world conditions • Benchmarking for calibration accuracy and reliability targets
Advanced Modules (Deployment, MLOps & Capstone)
Generative AI Productization: rollout sequencing & security • MLOps & Reliability: drift detection and incident triggers • Scale Engineering: balancing throughput and cost efficiency • Capstone: End-to-end execution and portfolio-grade artifact presentation
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python / TensorFlow |
| Covered Tool / Platform | Power BI |
| Covered Tool / Platform | MLflow |
| Covered Tool / Platform | ML Frameworks |
| Covered Tool / Platform | Computer Vision |
Frequently Asked Questions
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