Concepedia

Machine Learning-guided Protein Engineering

Machine Learning-Guided Protein Engineering is a computational approach within protein engineering that leverages machine learning algorithms to predict functional protein variants by analyzing sequence-structure-activity relationships, thereby guiding the design and optimization of enzymes in a data-driven manner.

64

Publications

5.6K

Citations

363

Authors

147

Institutions

Publications per year

2017–2026

61

Parents

Publications

64

Unified rational protein engineering with sequence-based deep representation learning

Ethan C. Alley, Grigory Khimulya, Surojit Biswas et al. · Nature Methods · 2019 · 1.2K citations · Full text

, , +12

Machine learning-assisted directed protein evolution with combinatorial libraries

Zachary Wu, S. B. Jennifer Kan, Russell D. Lewis et al. · Proceedings of the National Academy of Sciences · 2019 · 619 citations · Full text

, , +17

Low-N protein engineering with data-efficient deep learning

Surojit Biswas, Grigory Khimulya, Ethan C. Alley et al. · Nature Methods · 2021 · 417 citations

, , +14

Deep Dive into Machine Learning Models for Protein Engineering

Yuting Xu, Deeptak Verma, Robert P. Sheridan et al. · Journal of Chemical Information and Modeling · 2020 · 221 citations

, , +15

ECNet is an evolutionary context-integrated deep learning framework for protein engineering

Yunan Luo, Guangde Jiang, Tianhao Yu et al. · Nature Communications · 2021 · 186 citations · Full text

, , +13

1–5 of 64

Authors

363

  1. SB
    Surojit Biswas

    Harvard University, United States

    5 publications · 1.8K citations

    5

    h-index

  2. EC
    Ethan C. Alley

    Massachusetts Institute of Technology, United States

    5 publications · 1.8K citations

    5

    h-index

  3. FH
    Frances H. Arnold

    California Institute of Technology, United States

    5 publications · 862 citations

    5

    h-index

  4. GM
    George M. Church

    Harvard University, United States

    5 publications · 1.8K citations

    5

    h-index

  5. LH
    Liang Hong

    Shanghai Jiao Tong University, China

    4 publications · 196 citations

    4

    h-index

1–5 of 363

Institutions

147

  1. Massachusetts Institute of Technology

    Cambridge, United States

    6 h-index

    22 publications

    2.6K citations

  2. California Institute of Technology

    Pasadena, United States

    6 h-index

    32 publications

    4.6K citations

  3. Chinese Academy of Sciences

    Beijing, China

    5 h-index

    12 publications

    653 citations

  4. Harvard University

    Cambridge, United States

    5 h-index

    8 publications

    4.2K citations

  5. University of Wisconsin–Madison

    Madison, United States

    4 h-index

    24 publications

    1.1K citations

  6. Shanghai Artificial Intelligence Laboratory

    Shanghai, China

    4 h-index

    11 publications

    525 citations

  7. University of California, Berkeley

    Berkeley, United States

    4 h-index

    7 publications

    267 citations

  8. Shanghai Jiao Tong University

    Shanghai, China

    4 h-index

    23 publications

    1.1K citations

1–8 of 147

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 12 publications · 291 citations

    10

    h-index

  2. Nature Communications

    9 publications · 695 citations

    9

    h-index

  3. Journal of Chemical Information and Modeling

    JCIM · 4 publications · 319 citations

    4

    h-index

  4. ACS Synthetic Biology

    3 publications · 198 citations

    3

    h-index

  5. Nature Methods

    3 publications · 1.6K citations

    3

    h-index

1–5