📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Generative AI in Drug Discovery: From Molecular Design to Clinical Validation, from foundations to a certified capstone project.
Outline
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.
Outline
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.
Outline
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.
Outline
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.
Outline
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.
Outline
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.
Earn government-registered certification in Generative AI in Drug Discovery: From Molecular Design to Clinical Validation
e-Certificate and e-Marksheet issued on successful completion.
