Machine Learning in Enzyme Engineering

By Stanislav Mazurenko, Zbynek Prokop, Jirí Damborský. ACS Catalysis, 2019

359 citations recorded by the source corpus.

Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approache...

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8dd5b04e5f89f9bf26d93eef995bbc58e3d1de87

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Kevin Kaichuang Yang
2. Zachary Wu
3. Frances H. Arnold
4. Kadina E. Johnston
5. Bonnie Berger
6. Tristan Bepler

Citations in the corpus

• Protein sequence design with deep generative models (2021, methodology): “A developing paradigm, machine learning-guided protein engineering, promises to leverage the information obtained from wet-lab experiments with data-driven models to more e ffi ciently find desirable proteins [5, 6, 7] .”