Artículo
Optimal Partition of Datasets of QSPR Studies: A Sampling Problem
Fecha de publicación:
04/2010
Editorial:
Univ Kragujevac
Revista:
Match-communications In Mathematical And In Computer Chemistry
ISSN:
0340-6253
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Starting from different partitions of a 160 compounds dataset into training and test sets, we developed discriminant funtions to classify drugs into different categories of human intestinal absorptions rate. For each partition of the dataser, models that included up to ten Dragon descriptors were built, and the performance of each discriminante funtion in teh classification of the training and test sets was assessec and explores graphically through divergence diagrams. Results suggest that external validation tends to underestimate the predictive capability of QSAR models and that the more raliable results from external validation are obtained with even partitions of small and medium size datasets.
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Colecciones
Articulos(CCT - LA PLATA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - LA PLATA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - LA PLATA
Citación
Talevi, Alan; Bellera, Carolina Leticia; Castro, Eduardo Alberto; Bruno Blanch, Luis Enrique; Optimal Partition of Datasets of QSPR Studies: A Sampling Problem; Univ Kragujevac; Match-communications In Mathematical And In Computer Chemistry; 63; 3; 4-2010; 585-599
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