Linked Life Data

19128860

Source:http://linkedlifedata.com/resource/pubmed/id/19128860

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2009-5-4
pubmed:abstractText
Twenty-eight structurally diverse cytochrome 3A4 (CYP3A4) inhibitors have been subjected to quantitative structure-activity relationship (QSAR) studies. The analyses were performed with electronic, spatial, topological, and thermodynamic descriptors calculated using Cerius 2 version 10 software. The statistical tools used were linear [multiple linear regression with factor analysis as preprocessing step (FA-MLR), stepwise MLR, partial least squares (PLS), genetic function algorithm (GFA), genetic PLS (G/PLS)] and non-linear methods [artificial neural network (ANN)]. All the five linear modeling methods indicate the importance of n-octanol/water partition coefficient (logP) along with different topological and electronic parameters. The best model obtained from the training set (stepwise regression) based on highest external predictive R(2) value and lowest RMSEP value also showed good internal predictive power. Other models like FA-MLR, PLS, GFA and G/PLS are also of statistically significant internal and external validation characteristics. The best model [according to r(m)(2) for the test set, as defined by P.P. Roy, K. Roy, QSAR Comb. Sci. 27 (2008) 302-313] obtained from ANN showed a good r(2) value (determination coefficient between observed and predicted values) for the test set compounds, which was superior to those of other statistical models except the stepwise regression derived model. However, based upon the r(m)(2) value (test set), which penalizes a model for large differences between observed and predicted values, the stepwise MLR model was found to be inferior to other methods except PLS. Considering r(m)(2) value for the whole set, the G/PLS derived model appears to be the best predictive model for this data set. For choosing the best predictive model from among comparable models, r(m)(2) for the whole set calculated based on leave-one-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1768-3254
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2913-22
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques.
pubmed:affiliation
Department of Pharmaceutical Technology, Division of Medicinal and Pharmaceutical Chemistry, Drug Theoretics and Cheminformatics Laboratory, Jadavpur University, Kolkata, India. kunalroy_in@yahoo.com
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't