Machine Learning For Enzyme Engineering

Machine Learning for Enzyme Engineering is a computational methodology that integrates machine learning algorithms with biochemical data to predict and optimize enzyme functions, distinguishing itself by enabling data-driven design and directed evolution of enzymes with enhanced catalytic properties, specificity, or stability.

77

Publications

4.8K

Citations

422

Authors

187

Institutions

Publications per year

2017–2026

56

Authors

422

  1. UT
    Uwe T. Bornscheuer

    Universität Greifswald, Germany

    5 publications · 237 citations

    5

    h-index

  2. MB
    Mahiran Basri

    Universiti Putra Malaysia, Malaysia

    5 publications · 200 citations

    5

    h-index

  3. MJ
    Marian J. Menke

    Universität Greifswald, Germany

    3 publications · 125 citations

    3

    h-index

  4. HS
    Hal S. Alper

    The University of Texas at Austin, United States

    3 publications · 1.2K citations

    3

    h-index

  5. MB
    Mohd Basyaruddin Abdul Rahman

    Universiti Putra Malaysia, Malaysia

    3 publications · 131 citations

    3

    h-index

1–5 of 422

Venues

  1. Nature Communications

    7 publications · 540 citations

    7

    h-index

  2. Computational Biology and Chemistry

    CBAC · 5 publications · 184 citations

    5

    h-index

  3. Bioresource Technology

    5 publications · 199 citations

    5

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 67 citations

    3

    h-index

  5. ACS Synthetic Biology

    2 publications · 37 citations

    2

    h-index

1–5