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.
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38.5K
Citations
2.2K
Authors
899
Institutions
Publications per year
2017–2026
405
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564
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg et al. · Chemical Science · 2017 · 2.9K citations · Full text
Yasunobu Nohara, Koutarou Matsumoto, Hidehisa Soejima et al. · Computer Methods and Programs in Biomedicine · 2021 · 925 citations · Full text
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Centre National de la Recherche Scientifique
Paris, France
16 h-index
28 publications
3.1K citations
Inserm
Paris, France
13 h-index
27 publications
2.9K citations

Central South University
Changsha, China
12 h-index
41 publications
2.2K citations
Centre de Recherche en Cancérologie de Marseille
Marseille, France
11 h-index
15 publications
1.4K citations

Zhejiang University
Hangzhou, China
11 h-index
67 publications
3.8K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
10 h-index
36 publications
3.6K citations
Michigan State University
East Lansing, United States
10 h-index
27 publications
1.2K citations

Mahidol University
Bangkok, Thailand
9 h-index
23 publications
1.6K citations
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JCIM · 48 publications · 5K citations
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17 publications · 2K citations
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18 publications · 1.4K citations
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16 publications · 1.2K citations
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Repository · 13 publications · 1K citations
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