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Machine learning-based models to pbkp_redict modes of toxic action of phenols to Tetrahymena pyriformis
ArticleAbstract: The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic aPalabras claves:Machine learning technique, mode of toxic action, molecular descriptor, phenol derivative, Pollutant, QSARAutores:Garit J., Gerardo M. Casañola-Martin, Pham-The H., Stephen Jones Barigye, Torreblanca A., Torrens F.Fuentes:scopusQuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents
ArticleAbstract: The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic moPalabras claves:Atom-based quadratic indices, linear discriminant analysis, QSAR model, QuBiLs-MAS software, virtual screening, antifungal agentAutores:Acevedo-Barrios R., Echeverría-Díaz Y., García-Bernal M., Gerardo M. Casañola-Martin, Medina-Marrero R., Pérez-Giménez F., Stephen Jones Barigye, Torrens F., Yovani Marrero-PonceFuentes:scopusQuantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries
ArticleAbstract: Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immunPalabras claves:chemoinformatics, Histone deacetylase inhibitor, Machine learning, quantitative structure–activity relationship, Virtual ScreeningAutores:Gerardo M. Casañola-Martin, Karel Diéguez-Santana, Ngoc N.T., Nguyen-Hai N., Pham-The H., Thu H.L.T., Vu-Duc L.Fuentes:googlescopus