Course Overview: Welcome to the frontier of digital medicine. Developing a new drug from scratch takes over a decade and billions of dollars, but Drug Repurposing offers a faster, smarter alternative. This course is meticulously designed to teach you how to apply Deep Learning (DL) to identify new therapeutic uses for approved drugs. By bridging biological insight with advanced algorithmic power, we unlock the potential to treat diseases faster than ever before.

What You Will Learn: We have structured the curriculum into 3 distinct modules covering 10 comprehensive classes, guiding you from data preparation to complex model deployment.

Module 1: Foundations & Data Strategy: Understand the "data fuel" behind the AI engine. Learn to navigate massive biomedical databases (like ChEMBL and DrugBank) and master the art of converting chemical structures into machine-readable formats (SMILES strings and molecular graphs).

Module 2: Deep Learning Architectures: Dive into the "brain" of the operation. You will explore cutting-edge architectures specifically suited for chemistry, such as Graph Neural Networks (GNNs) for analyzing molecular geometry and Autoencoders for condensing complex biological data into useful features.

Module 3: Advanced Applications & Real-World Validation: Apply your models to solve real problems. Learn to build Knowledge Graphs that map hidden connections between drugs and diseases, and study real-world success stories where AI successfully identified treatments for pandemics and rare diseases.

Who Should Attend:

• Bioinformatics and Computational Biology students.

• Data Scientists interested in healthcare applications.

• Pharmaceutical researchers looking to adopt AI-driven methodologies.

Outcome: By the end of this course, you will be able to build and train Deep Learning models to predict drug-target interactions, analyze molecular graphs, and propose scientifically valid candidates for drug repurposing projects.