Computational Developability Prediction

Computational Developability Prediction is a scholarly methodological framework that systematically employs computational models and algorithms to quantitatively assess the likelihood of successful development and manufacturability of therapeutic candidates, particularly biopharmaceuticals, by predicting physicochemical and biological properties indicative of developability.

14

Publications

1.1K

Citations

86

Authors

27

Institutions

Publications per year

2017–2026

11

Authors

86

  1. CM
    Charlotte M. Deane

    University of Oxford, United Kingdom

    3 publications · 432 citations

    3

    h-index

  2. MI
    Matthew I. J. Raybould

    University of Oxford, United Kingdom

    3 publications · 432 citations

    3

    h-index

  3. KK
    Konrad Krawczyk

    University of Oxford, United Kingdom

    2 publications · 394 citations

    2

    h-index

  4. PZ
    Pavol Zlatoidský

    State Institute for Drug Control, Slovakia

    1 publications · 34 citations

    1

    h-index

  5. RA
    Rahmad Akbar

    University of Oslo, Norway

    1 publications · 26 citations

    1

    h-index

1–5 of 86

Venues

  1. mAbs

    3 publications · 143 citations

    3

    h-index

  2. Communications Biology

    2 publications · 65 citations

    2

    h-index

  3. Molecular Pharmaceutics

    1 publications · 69 citations

    1

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 1 publications · 28 citations

    1

    h-index

  5. Nature Communications

    1 publications · 34 citations

    1

    h-index

1–5