Documents disponibles écrits par cet auteur

Quantitative structure−property relationship (QSPR) prediction of liquid viscosities of pure organic compounds employing random forest regression / Remya Rajappan in Industrial & engineering chemistry research, Vol. 48 N° 21 (Novembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9708–9712 Titre : Quantitative structure−property relationship (QSPR) prediction of liquid viscosities of pure organic compounds employing random forest regression Type de document : texte imprimé Auteurs : Remya Rajappan, Auteur ; Prashant D. Shingade, Auteur ; Ramanathan Natarajan, Auteur Année de publication : 2010 Article en page(s) : pp. 9708–9712 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Pure  organic  liquids  Quantitative  structure−property  relationship  Robust  Random  Forest  regression  algorithm Résumé : A quantitative structure−property relationship (QSPR) approach was used to develop a predictive model for viscosities of pure organic liquids using a set of 403 compounds that belong to diverse classes of organic chemicals. A pool of 116 descriptors that encode topostructural, topochemical, electrotopological, geometrical, and quantum chemical properties of the organic compounds was used to develop QSPR models, based on the robust Random Forest (RF) regression algorithm. The performance of the algorithm, in terms of correlation coefficients and mean square errors, was determined to be good. The capability of the algorithm to build models and select the most-informative features simultaneously is very useful for several quantitative structure−activity/property relationship tasks. The eight most-dominant features selected by the RF regression algorithm primarily contained predictors that encode characteristics of atoms and groups that form hydrogen bonds, as well as factors involving molecular shape and size. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018406 [article] Quantitative structure−property relationship (QSPR) prediction of liquid viscosities of pure organic compounds employing random forest regression [texte imprimé] / Remya Rajappan, Auteur ; Prashant D. Shingade, Auteur ; Ramanathan Natarajan, Auteur . - 2010 . - pp. 9708–9712. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9708–9712 Mots-clés : Pure  organic  liquids  Quantitative  structure−property  relationship  Robust  Random  Forest  regression  algorithm Résumé : A quantitative structure−property relationship (QSPR) approach was used to develop a predictive model for viscosities of pure organic liquids using a set of 403 compounds that belong to diverse classes of organic chemicals. A pool of 116 descriptors that encode topostructural, topochemical, electrotopological, geometrical, and quantum chemical properties of the organic compounds was used to develop QSPR models, based on the robust Random Forest (RF) regression algorithm. The performance of the algorithm, in terms of correlation coefficients and mean square errors, was determined to be good. The capability of the algorithm to build models and select the most-informative features simultaneously is very useful for several quantitative structure−activity/property relationship tasks. The eight most-dominant features selected by the RF regression algorithm primarily contained predictors that encode characteristics of atoms and groups that form hydrogen bonds, as well as factors involving molecular shape and size. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018406