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
Parents
33
1–5 of 33
164
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66
Université Nangui Abrogoua
Abidjan, Ivory Coast
2 h-index
2 publications
159 citations
ETH Zurich
Zurich, Switzerland
2 h-index
14 publications
1.3K citations
Goethe University Frankfurt
Frankfurt am Main, Germany
2 h-index
2 publications
191 citations
Sanofi (Germany)
Frankfurt am Main, Germany
2 h-index
7 publications
215 citations
Max Planck Institute of Biochemistry
Martinsried, Germany
2 h-index
10 publications
790 citations
University of Buea
Buea, Cameroon
2 h-index
7 publications
576 citations
University of Douala
Douala, Cameroon
2 h-index
8 publications
649 citations
University of Pittsburgh
Pittsburgh, United States
2 h-index
8 publications
632 citations
1–8 of 66
4 publications · 148 citations
4
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3 publications · 126 citations
3
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2 publications · 233 citations
2
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JCIM · 2 publications · 53 citations
2
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Repository · 1 publications · 56 citations
1
h-index
1–5