Deep Learning for Protein Folding & Drug Design

Duration:

8 Weeks (1 Module per Week)

Prerequisites:

• Intermediate knowledge of machine learning and deep learning (feed-forward networks, CNNs, RNNs, attention-based models)
• Basic understanding of molecular biology, protein structure, and drug discovery concepts
• Some programming experience (Python preferred)

Learning Objectives:

By the end of this course, students will be able to:

1. Understand the fundamentals of protein structure, folding principles, and the challenges that deep learning can address.
2. Apply modern deep learning models (e.g., attention-based transformers, graph neural networks) to protein folding and structural predictions.
3. Integrate protein structure information into drug design pipelines, including docking, virtual screening, and lead optimization.
4. Evaluate deep learning models for accuracy, interpretability, and applicability to real-world drug discovery problems.
5. Stay informed about the latest research and future directions in AI-driven protein engineering and pharmaceutical R&D.