Generative Protein Design

Generative Protein Design is a computational approach within protein engineering that, as a distinct paradigm under the broader field of protein design modeling, employs generative algorithms to create novel protein sequences or structures by learning underlying patterns and physical principles from existing protein data.

64

Publications

5.8K

Citations

285

Authors

81

Institutions

Publications per year

2017–2026

62

Authors

285

  1. PH
    Po‐Ssu Huang

    Stanford University, United States

    7 publications · 543 citations

    7

    h-index

  2. MJ
    Markus J. Buehler

    Massachusetts Institute of Technology, United States

    6 publications · 238 citations

    6

    h-index

  3. RR
    Raphael R. Eguchi

    Stanford University, United States

    5 publications · 461 citations

    5

    h-index

  4. AM
    Ali Madani

    Salesforce (United States), United States

    4 publications · 343 citations

    4

    h-index

  5. BH
    Birte Höcker

    University of Bayreuth, Germany

    3 publications · 990 citations

    3

    h-index

1–5 of 285

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 20 publications · 1K citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 9 publications · 353 citations

    9

    h-index

  3. Nature Communications

    5 publications · 1.3K citations

    5

    h-index

  4. Science

    3 publications · 690 citations

    3

    h-index

  5. Nature Machine Intelligence

    3 publications · 480 citations

    3

    h-index

1–5