• español
    • English
  • English 
    • español
    • English
  • Login
View Item 
  •   DSpace Home
  • Producción Científica
  • Artículos, capítulos, libros...UCO
  • View Item
  •   DSpace Home
  • Producción Científica
  • Artículos, capítulos, libros...UCO
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes

Thumbnail
View/Open
molecules-23-02756-v2.pdf (2.196Mb)
Author
Luque Ruiz, Irene
Gómez-Nieto, Miguel Ángel
Publisher
MDPI
Date
2018
Subject
QSAR
Classification model
Applicability domain
Rivality index
Modelability index
METS:
Mostrar el registro METS
PREMIS:
Mostrar el registro PREMIS
Metadata
Show full item record
Abstract
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms
URI
http://hdl.handle.net/10396/17393
Fuente
Molecules 23, 2756 (2018)
Versión del Editor
http://dx.doi.org/10.3390/molecules23112756
Collections
  • DIAN-Artículos, capítulos...
  • Artículos, capítulos, libros...UCO

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
© Biblioteca Universidad de Córdoba
Biblioteca  UCODigital
 

 

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

LoginRegister

Statistics

View Usage Statistics

De Interés

Archivo Delegado/AutoarchivoAyudaPolíticas de Helvia

Compartir


DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
© Biblioteca Universidad de Córdoba
Biblioteca  UCODigital