Comparación de modelos novedosos de proximidad en quimioinformática

Oscar Miguel Rivera Borroto, Yoandy Hernández Díaz, José Manuel García De La Vega, Ricardo Del Corazón Grau Ábalo, Yovani Marrero Ponce

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

This work comprises the computational implementation in the Java environment of 21 proximity models to be used in simulated experiments of similarity searching, nine out of which are novel in Chemoinformatics since they come from the psychology field, and other 12 are measures already established in the specialized literature. Afterwards, the similarity measures were compared and assessed at the "early retrieval" using nine data sets from medicinal chemistry, represented by machine learning-selected real descriptors, and one efficient matching algorithm. Results show that in average trends the new models perform superiorly with respect to the reference ones, and more than half of them are among the top-10 models.

Título traducido de la contribuciónComparison of novelproximity models in Chemoinformatics
Idioma originalEspañol
Páginas (desde-hasta)272-277
Número de páginas6
PublicaciónAfinidad
Volumen69
N.º560
EstadoPublicada - oct. 2012
Publicado de forma externa

Palabras clave

  • Machine-learning descriptor selection
  • Medicinal chemistry data set
  • Proximity model
  • Similarity searching

Huella

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