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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...
| Autores principales: | , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2019
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| Materias: | |
| 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 |