Machine Learning Scoring Models

Machine Learning Scoring Models is a computational approach within virtual screening that employs statistical learning algorithms to predict the binding affinity or biological activity of small molecules toward a target protein, distinguished from classical scoring functions by its reliance on training data and complex, non-linear feature representations to improve prediction accuracy.

564

Publications

38.5K

Citations

2.2K

Authors

899

Institutions

Publications per year

2017–2026

405

Authors

2.2K

  1. PJ
    Pedro J. Ballester

    Inserm, France

    14 publications · 1.8K citations

    14

    h-index

  2. TH
    Tingjun Hou

    Zhejiang University, China

    11 publications · 840 citations

    11

    h-index

  3. HL
    Hongjian Li

    Chinese University of Hong Kong, Hong Kong SAR China

    9 publications · 1K citations

    9

    h-index

  4. DC
    Dongsheng Cao

    Central South University, China

    9 publications · 639 citations

    9

    h-index

  5. CS
    Chao Shen

    Zhejiang University, China

    9 publications · 773 citations

    9

    h-index

1–5 of 2.2K

Venues

  1. Journal of Chemical Information and Modeling

    JCIM · 48 publications · 5K citations

    32

    h-index

  2. Bioinformatics

    17 publications · 2K citations

    17

    h-index

  3. BMC Bioinformatics

    18 publications · 1.4K citations

    16

    h-index

  4. Briefings in Bioinformatics

    16 publications · 1.2K citations

    15

    h-index

  5. arXiv (Cornell University)

    Repository · 13 publications · 1K citations

    12

    h-index

1–5