Machine Learning Strategies When Transitioning between Biological Assays.
Journal
Journal of chemical information and modeling
ISSN: 1549-960X
Titre abrégé: J Chem Inf Model
Pays: United States
ID NLM: 101230060
Informations de publication
Date de publication:
26 07 2021
26 07 2021
Historique:
pubmed:
22
6
2021
medline:
10
8
2021
entrez:
21
6
2021
Statut:
ppublish
Résumé
Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and Na
Identifiants
pubmed: 34152755
doi: 10.1021/acs.jcim.1c00293
pmc: PMC8317157
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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