![]() ![]() Thus, the ML4Protein Engineering community runs a bi-weekly seminar series to address these advances and other outstanding problems, such as high-throughput screening, model-based optimization, and representation learning. We think these questions will be best addressed by a collaborative, interdisciplinary community. How do we use our trained models to guide data collection? What are the limits of the growing structural and evolutionary data in the PDB and UniProt? Stanford is participating in a pilot program for Slack Huddles, a new social audio feature that lets you talk, live and ad-hoc, with your teammates in a Slack channel. Which machine learning models and parameterizations of proteins hold the right inductive biases? What experimental approaches can feed the data-driven design cycle? However, this excitement has exposed important research questions across the foundation of this emerging engineering discipline. ![]() These advances may enable more rapid development of designed proteins with applications ranging from biopharmaceuticals, catalysis, material design and basic science research. Recent advances in high-throughput experimental methods and machine learning approaches have fueled interest in ML-driven protein design. ![]()
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