INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS

A new internal cross-validation method is presented for assessing the true predictive capability of QSAR models. The test is general and can be applied in many QSAR/QSPR approaches. In this work, the method is tested on a well-known benchmark set of steroids. In order to make the calculations, Topol...

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Autores principales: BESALÚ,EMILI, VERA,LEONEL
Lenguaje:English
Publicado: Sociedad Chilena de Química 2008
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Acceso en línea:http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072008000300005
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spelling oai:scielo:S0717-970720080003000052008-10-23INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDSBESALÚ,EMILIVERA,LEONEL benchmark steroids cross-validation predictions internal test set method statistical validation topological quantum similarity indexes A new internal cross-validation method is presented for assessing the true predictive capability of QSAR models. The test is general and can be applied in many QSAR/QSPR approaches. In this work, the method is tested on a well-known benchmark set of steroids. In order to make the calculations, Topological Quantum Similarity índices and Múltiple Linear Regression models were consideredinfo:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.53 n.3 20082008-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072008000300005en10.4067/S0717-97072008000300005
institution Scielo Chile
collection Scielo Chile
language English
topic benchmark steroids
cross-validation
predictions
internal test set method
statistical validation
topological quantum similarity indexes
spellingShingle benchmark steroids
cross-validation
predictions
internal test set method
statistical validation
topological quantum similarity indexes
BESALÚ,EMILI
VERA,LEONEL
INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS
description A new internal cross-validation method is presented for assessing the true predictive capability of QSAR models. The test is general and can be applied in many QSAR/QSPR approaches. In this work, the method is tested on a well-known benchmark set of steroids. In order to make the calculations, Topological Quantum Similarity índices and Múltiple Linear Regression models were considered
author BESALÚ,EMILI
VERA,LEONEL
author_facet BESALÚ,EMILI
VERA,LEONEL
author_sort BESALÚ,EMILI
title INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS
title_short INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS
title_full INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS
title_fullStr INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS
title_full_unstemmed INTERNAL TEST SET (ITS) METHOD: A NEW CROSS-VALIDATION TECHNIQUE TO ASSESS THE PREDICTIVE CAPABILITY OF QSAR MODELS. APPLICATION TO A BENCHMARK SET OF STEROIDS
title_sort internal test set (its) method: a new cross-validation technique to assess the predictive capability of qsar models. application to a benchmark set of steroids
publisher Sociedad Chilena de Química
publishDate 2008
url http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072008000300005
work_keys_str_mv AT besaluemili internaltestsetitsmethodanewcrossvalidationtechniquetoassessthepredictivecapabilityofqsarmodelsapplicationtoabenchmarksetofsteroids
AT veraleonel internaltestsetitsmethodanewcrossvalidationtechniquetoassessthepredictivecapabilityofqsarmodelsapplicationtoabenchmarksetofsteroids
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