Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-3-9
pubmed:abstractText
Quantitative structure-activity relationship (QSAR) models were constructed for predicting the inhibition of furin-dependent processing of anthrax protective antigen of substituted guanidinylated aryl 2,5-dideoxystreptamines. Molecular descriptors calculated by E-Dragon and RECON were subjected to variable reduction using the Unsupervised Forward Selection (UFS) algorithm. The variables were then used as input for QSAR model generation using partial least squares and back-propagation neural network. Prediction was performed via a two-step approach: (i) perform classification to determine whether the molecule is active or inactive, (ii) develop a QSAR regression model of active molecules. Both classification and regression models yielded good results with RECON providing higher accuracy than that of E-DRAGON descriptors. The performance of the regression model using E-Dragon and RECON descriptors provided a correlation coefficient of 0.807 and 0.923 and root mean square error of 0.666 and 0.304, respectively. Interestingly, it was observed that appropriate representations of the protonation states of the molecules were crucial for good prediction performance, which coincides with the fact that the inhibitors interact with furin via electrostatic forces. The results provide good prospect of using the proposed QSAR models for the rational design of novel therapeutic furin inhibitors toward anthrax and furin-dependent diseases.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1768-3254
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1664-73
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Modeling the activity of furin inhibitors using artificial neural network.
pubmed:affiliation
Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't