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pro vyhledávání: '"María Virginia Sabando"'
Autor:
María Jimena Martínez, María Virginia Sabando, Axel J. Soto, Carlos Roca, Carlos Requena-Triguero, Nuria E. Campillo, Juan A. Páez, Ignacio Ponzoni
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
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29 p.2 fig.-3 tab.-1 graph.abst.+Sup. Inf. 4 p._4 tab.
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strai
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strai
Autor:
María Jimena Martínez, María Virginia Sabando, Axel J. Soto, Carlos Roca, Carlos Requena-Triguero, Nuria E. Campillo, Juan A. Páez, Ignacio Ponzoni
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the publi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94711bb2931131ae74eccacf22d2df37
https://doi.org/10.26434/chemrxiv-2022-852tf
https://doi.org/10.26434/chemrxiv-2022-852tf
Autor:
Axel J. Soto, Ignacio Ponzoni, Jan Mican, María Luján Ganuza, Barbora Kozlíková, Pavol Ulbrich, Matías Nicolás Selzer, María Virginia Sabando, Jan Byška
Publikováno v:
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently p
Publikováno v:
Applied Soft Computing. 85:105777
In the fields of pharmaceutical research and biomedical sciences, QSAR modeling is an established approach during drug discovery for prediction of biological activity of drug candidates. Yet, QSAR modeling poses a series of open challenges. First, ch