647 citations · 796 across the 2 of their papers we have counts for
4 papers
Machine Learning for Protein Engineering
Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann +3
Directed evolution of proteins has been the most effective method for protein engineering. However, a new paradigm is emerging, fusing the library generation and screening approach…
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold +1
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge…
Machine learning-assisted directed protein evolution with combinatorial libraries
Zachary Wu, S. B. Jennifer Kan, Russell D. Lewis +2
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate mac…
Machine learning-guided directed evolution for protein engineering
Kevin K. Yang, Zachary Wu, Frances H. Arnold
Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence…