Generative AI for Drug Discovery and Protein Folding

The intersection of artificial intelligence and biotechnology is revolutionizing how we discover life-saving medicines and understand biology. This text-based course introduces you to the foundational concepts of generative AI applied to molecular design and protein folding. You will transition from a curious learner to understanding the computational workflows that modern researchers use to generate novel chemical compounds and predict complex protein structures. By studying clear written explanations, conceptual breakdowns, and practical code snippets, you will grasp the mechanics of molecular graph models and deep learning architectures. What you'll learn: - Understand the core principles of molecular representation, including SMILES strings and molecular graphs. - Explore how generative neural networks, such as variational autoencoders and diffusion models, design new drug-like molecules. - Learn the fundamentals of Graph Neural Networks (GNNs) and their role in predicting molecular properties. - Discover the mechanics behind protein folding predictions and how transformer-based models analyze amino acid sequences. - Analyze modern workflows in computer-aided drug design, from target identification to lead optimization. The course begins with essential biological and chemical definitions before guiding you through neural network architectures, molecular generation strategies, and modern protein structure prediction concepts. You will progress from foundational theory to reviewing conceptual code implementations of molecular models. This course is designed for beginners in bioinformatics, software developers transitioning to biotech, and student researchers looking for a clear, conceptual entry point into AI-driven drug discovery. No advanced background in biochemistry or deep learning is required. Start reading today to unlock the potential of AI in modern medicine.