Machine Learning Drug Repurposing

Machine Learning Drug Repurposing is a computational methodology that applies machine learning algorithms to identify novel therapeutic uses for existing drugs by analyzing biological, chemical, and clinical data, distinguishing it from traditional drug discovery through its reliance on predictive modeling and data-driven inference.

176

Publications

12.1K

Citations

842

Authors

410

Institutions

Publications per year

2017–2026

162

Authors

842

  1. SE
    Sean Ekins

    Collaborations Pharmaceuticals (United States), United States

    6 publications · 1K citations

    6

    h-index

  2. TR
    Thomas R. Lane

    Collaborations Pharmaceuticals (United States), United States

    4 publications · 672 citations

    4

    h-index

  3. KH
    Kexin Huang

    Harvard University, United States

    4 publications · 224 citations

    4

    h-index

  4. DC
    Dongsheng Cao

    Central South University, China

    4 publications · 308 citations

    4

    h-index

  5. KM
    Kimberley M. Zorn

    Collaborations Pharmaceuticals (United States), United States

    4 publications · 672 citations

    4

    h-index

1–5 of 842

Venues

  1. Methods in molecular biology

    Book series · 9 publications · 247 citations

    9

    h-index

  2. Computers in Biology and Medicine

    6 publications · 194 citations

    6

    h-index

  3. Briefings in Bioinformatics

    6 publications · 514 citations

    5

    h-index

  4. Nature Machine Intelligence

    4 publications · 903 citations

    4

    h-index

  5. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 4 publications · 211 citations

    4

    h-index

1–5