647 citations · 657 across the 3 of their papers we have counts for
5 papers
Advances in Machine Learning for Directed Evolution: A Five-Year Retrospective
Bruce J. Wittmann
The last five-plus years have seen many protein engineering disciplines transformed by advances in machine learning (ML), but the same cannot be said for directed evolution. Reflec…
Steering Generative Models with Experimental Data for Protein Fitness Optimization
Jason Yang, Wenda Chu, Daniel Khalil +4
Protein fitness optimization involves finding a protein sequence that maximizes desired quantitative properties in a combinatorially large design space of possible sequences. Recen…
CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
Jason Yang, Ariane Mora, Shengchao Liu +4
Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are…
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…
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…