Predicting kinase inhibitors using bioactivity matrix derived informer sets

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prior...

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Detalles Bibliográficos
Autores principales: Zhang, Huikun, Ericksen, Spencer S., Lee, Ching-pei, Ananiev, Gene E., Wlodarchak, Nathan, Yu, Peng, Mitchell, Julie C., Gitter, Anthony, Wright, Stephen J., Hoffmann, F. Michael, Wildman, Scott A., Newton, Michael A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695194/
https://www.ncbi.nlm.nih.gov/pubmed/31381559
http://dx.doi.org/10.1371/journal.pcbi.1006813

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695194/
https://www.ncbi.nlm.nih.gov/pubmed/31381559
http://dx.doi.org/10.1371/journal.pcbi.1006813

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