Machine Learning-Based Design of Proteins
Explore the cutting-edge intersection of machine learning and protein engineering in this 32-minute talk by Jennifer Listgarten from UC Berkeley. Delve into the challenges of navigating the vast combinatorial space of protein design and learn how directed evolution and predictive models work synergistically to overcome these obstacles. Discover the concept of epistemic uncertainty and its role in library design, followed by a real-life example demonstrating the optimization problem in protein engineering. Gain insights into the algorithm description, the language of probability, and its applications in gene therapy. Understand the innovative approaches used to tackle complex protein design challenges and their potential impact on future scientific advancements.