Library Diversity Optimization

Library Diversity Optimization is a methodological framework in antibody library construction and display technologies that systematically enhances the representational breadth and quality of antibody variants within a library to maximize the likelihood of identifying high-affinity binders during selection processes.

17

Publications

591

Citations

82

Authors

33

Institutions

Publications per year

2017–2026

4

Authors

82

  1. DS
    David Shin

    University of California, San Francisco, United States

    2 publications · 65 citations

    2

    h-index

  2. DH
    David H. Brookes

    University of California, Berkeley, United States

    2 publications · 65 citations

    2

    h-index

  3. DZ
    Danqing Zhu

    Stanford University, United States

    2 publications · 65 citations

    2

    h-index

  4. MW
    Marvin Waldman

    Simulations Plus (United States), United States

    2 publications · 48 citations

    2

    h-index

  5. JL
    Jennifer Listgarten

    Microsoft (United States), United States

    2 publications · 65 citations

    2

    h-index

1–5 of 82

Venues

  1. 3

    h-index

  2. Protein Engineering Design and Selection

    PEDS · 2 publications · 56 citations

    2

    h-index

  3. Journal of Molecular Graphics and Modelling

    2 publications · 48 citations

    2

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 2 publications · 43 citations

    2

    h-index

  5. Tetrahedron Letters

    1 publications · 33 citations

    1

    h-index

1–5