Computational Lead Generation

Computational Lead Generation is a systematic methodology within lead compound design that employs computational techniques to identify and prioritize novel chemical entities with potential biological activity, distinguishing itself from experimental approaches by leveraging in silico models for efficient exploration of chemical space.

33

Publications

1.7K

Citations

164

Authors

66

Institutions

Publications per year

2017–2026

13

Authors

164

  1. YH
    Yijun Huang

    University of Pittsburgh, United States

    2 publications · 158 citations

    2

    h-index

  2. EM
    Eugène Megnassan

    Université Nangui Abrogoua, Côte d’Ivoire

    2 publications · 159 citations

    2

    h-index

  3. AD
    Alexander Dömlingꝉ

    University of Groningen, Netherlands

    2 publications · 158 citations

    2

    h-index

  4. GS
    Gisbert Schneider

    ETH Zurich, Switzerland

    2 publications · 191 citations

    2

    h-index

  5. GM
    Grzegorz M. Popowicz

    Helmholtz Zentrum München, Germany

    2 publications · 158 citations

    2

    h-index

1–5 of 164

Venues

  1. Journal of Medicinal Chemistry

    4 publications · 148 citations

    4

    h-index

  2. Journal of Cheminformatics

    3 publications · 126 citations

    3

    h-index

  3. Angewandte Chemie International Edition

    2 publications · 233 citations

    2

    h-index

  4. Journal of Chemical Information and Modeling

    JCIM · 2 publications · 53 citations

    2

    h-index

  5. PubMed

    Repository · 1 publications · 56 citations

    1

    h-index

1–5