Programme Parameters
Advanced Postgrad
3 Days (4.5 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
This 3-day course on Generative AI in Drug Discovery explores how cutting-edge AI models are reshaping pharmaceutical research, from molecular design to clinical validation. Participants will learn the fundamentals of generative AI, including GANs and VAEs, and their application in predicting molecular properties, designing drug-like compounds, and optimizing leads. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3-day course on Generative AI in Drug Discovery explores how cutting-edge AI models are reshaping pharmaceutical research, from molecular design to clinical validation. Participants will learn the fundamentals of generative AI, including GANs and VAEs, and their application in predicting molecular properties, designing drug-like compounds, and optimizing leads.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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 Generative AI in Drug Discovery
Understand the core concepts and importance of Generative AI in scientific research. • Differentiate between traditional and AI-driven drug discovery pipelines. • Explore key applications of AI in molecular design and lead optimization.
Generative Models (GANs and VAEs) & Molecular Generation
Learn the principles of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). • Apply GANs and VAEs for de novo molecular design and generation. • Conduct hands-on exercises using DeepChem/RDKit to generate novel molecules.
Molecular Property Prediction
Utilize AI models for accurate prediction of ADMET properties, solubility, and toxicity. • Evaluate molecular binding affinity using advanced AI techniques. • Perform practical property prediction with RDKit/DeepChem.
Virtual Screening and Drug Repurposing
Employ AI for efficient large-scale virtual screening of compound libraries. • Discover new therapeutic uses for existing drugs through AI-driven repurposing strategies. • Implement AI tools to screen molecules for bioactivity.
AI in Preclinical and Clinical Development
Leverage AI for toxicity assessment and efficacy prediction in preclinical studies. • Identify and validate biomarkers using AI for enhanced clinical understanding. • Predict clinical trial success rates through robust AI models.
Challenges, Opportunities, and Ethics in AI-Driven Drug Discovery
Analyze the current challenges and future opportunities in applying AI to drug discovery. • Discuss the ethical considerations and responsible implementation of AI in pharmaceuticals. • Formulate strategies for integrating AI into real-world drug development pipelines.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RDKit |
| Covered Tool / Platform | DeepChem |
| Covered Tool / Platform | GANs |
| Covered Tool / Platform | VAEs |
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