Formulation Data Mining

Formulation Data Mining is a computational methodology that systematically extracts, analyzes, and interprets patterns, relationships, and knowledge from formulation science data to optimize drug product development.

26

Publications

1K

Citations

101

Authors

48

Institutions

Publications per year

2017–2026

12

Authors

101

  1. RR
    R.C. Rowe

    AstraZeneca (United Kingdom), United Kingdom

    4 publications · 109 citations

    4

    h-index

  2. PE
    Pierre Esseiva

    University of Lausanne, Switzerland

    2 publications · 143 citations

    2

    h-index

  3. CA
    Christine Allen

    University of Toronto, Canada

    2 publications · 53 citations

    2

    h-index

  4. QS
    Qiyun Shao

    University of Bradford, United Kingdom

    2 publications · 49 citations

    2

    h-index

  5. JD
    Jens Dhondt

    Ghent University, Belgium

    2 publications · 113 citations

    2

    h-index

1–5 of 101

Venues

  1. International Journal of Pharmaceutics

    4 publications · 236 citations

    4

    h-index

  2. Journal of Pharmaceutical Sciences

    3 publications · 53 citations

    3

    h-index

  3. Forensic Science International

    2 publications · 143 citations

    2

    h-index

  4. Pharmaceutical Science & Technology Today

    2 publications · 60 citations

    2

    h-index

  5. The Journal of Phytopharmacology

    1 publications · 28 citations

    1

    h-index

1–5