Generative AI in Drug Discovery: From Molecular Design to Clinical Validation
Empowering Next-Gen Drug Discovery through Generative AI
Skills you will gain:
About Program:
This 3-day workshop 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. Through interactive sessions and hands-on demos with tools such as RDKit and DeepChem, attendees will gain practical skills in molecular generation, screening, and property prediction. Real-world case studies and discussions on challenges, opportunities, and ethics will prepare participants to apply AI effectively in drug repurposing, biomarker discovery, and personalized medicine, bridging the gap between computation and clinical translation.
Aim: To equip participants with foundational and advanced knowledge of generative AI applications in drug discovery, enabling them to understand, experiment, and apply AI models for molecular design, optimization, and validation
Program Objectives:
To introduce participants to the fundamentals of generative AI and its application in drug discovery. Participants will learn to use AI models for molecular design, property prediction, and lead optimization. Hands-on sessions will provide practical experience with AI tools like RDKit and DeepChem. The workshop aims to equip attendees with the skills to apply AI in real-world drug discovery and development.
What you will learn?
Day 1: Introduction to Generative AI in Drug Discovery
- Basics of Generative AI: What it is, why it matters in science, key model types (GANs, VAEs).
- Drug Discovery Pipeline: Traditional workflow vs. AI-driven approaches.
- Applications: AI in molecular design, drug-likeness prediction, and lead optimization.
- Case Study: AI successes in drug repurposing and new candidate identification.
- Hands-On: Intro to DeepChem/RDKit; generate simple molecules.
- Discussion: Challenges and future directions.
Day 2: Advanced AI Techniques
- Molecular Property Prediction: Using AI for ADMET, solubility, toxicity, binding affinity.
- Generative Models: How AI designs drug-like molecules (GANs, VAEs).
- Practical Demo: Property prediction with RDKit/DeepChem.
- Discussion: Real-time decision making and AI-guided drug repurposing.
Day 3: Design to Validation
- Virtual Screening & Repurposing: AI for large-scale screening and finding new uses of existing drugs.
- Preclinical & Clinical Support: AI in toxicity, efficacy, biomarker discovery, and trial success prediction.
- Hands-On: Screening molecules for bioactivity using AI tools.
- Final Wrap-Up: Future of AI in pharma—opportunities, challenges, ethics.
Mentor Profile
Fee Plan
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Intended For :
- Students and PhD Scholars in biotechnology, bioinformatics, pharmaceutical sciences, and computational biology who want to explore AI applications in drug discovery.
- Researchers and Academicians interested in integrating AI-driven tools into their scientific projects.
- Industry Professionals working in pharma, biotech, and healthcare R&D looking to understand the potential of generative AI in drug development.
- Data Scientists and AI Enthusiasts eager to apply machine learning and generative models to real-world biomedical challenges.
Career Supporting Skills
Program Outcomes