Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/41807
metadata.artigo.dc.title: MIA-QSAR, PC-Ranking and least-squares support-vector machines in the accurate prediction of the activities of Phosphodiesterase Type 5 (PDE-5) inhibitors
metadata.artigo.dc.creator: Goodarzi, Mohammad
Freitas, Matheus P.
metadata.artigo.dc.subject: MIA-QSAR
PCA ranking
LS-SVM
PDE-5
Multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR)
Principal component analysis (PCA)
Least squares support vector machine (LS-SVM)
metadata.artigo.dc.publisher: Taylor & Francis
metadata.artigo.dc.date.issued: Oct-2010
metadata.artigo.dc.identifier.citation: GOODARZI, M.; FREITAS, M. P. MIA-QSAR, PC-Ranking and least-squares support-vector machines in the accurate prediction of the activities of Phosphodiesterase Type 5 (PDE-5) inhibitors. Molecular Simulation, [S.l.], v. 36, n. 11, p. 871-877, Oct. 2010. DOI: 10.1080/08927022.2010.490261.
metadata.artigo.dc.description.abstract: Phosphodiesterase type-5 (PDE-5) is a key enzyme involved in the erection process. PDE-5 inhibitors, such as Sildenafil (ViagraTM), Vardenafil (LevitraTM) and Tadalafil (CialisTM), are used for the treatment of erectile dysfunction. Computer-assisted modelling of biological activities of PDE-5 inhibitors may make quantitative structure–activity relationship (QSAR) models useful for the development of safer (low side effects) and more potent drugs. The multivariate image analysis applied to QSAR (MIA-QSAR) method, coupled to partial least-squares (PLS) regression, has provided highly predictive QSAR models. Nevertheless, regression methods which take into account nonlinearity, such as least-squares support-vector machines (LS-SVMs), are supposed to predict biological activities more accurately than the usual linear methods. Thus, together with prior variable selection using principal component analysis ranking, MIA-QSAR and LS-SVM regression were applied to model the bioactivities of a series of cyclic guanine derivatives (PDE-5 inhibitors), and the results were compared with those based on linear methodologies. MIA-QSAR/LS-SVM was found to improve greatly the prediction performance when compared with MIA-QSAR/PLS, MIA-QSAR/N-PLS, CoMFA/PLS and CoMSIA/PLS models.
metadata.artigo.dc.identifier.uri: https://www.tandfonline.com/doi/full/10.1080/08927022.2010.490261
http://repositorio.ufla.br/jspui/handle/1/41807
metadata.artigo.dc.language: en_US
Appears in Collections:DQI - Artigos publicados em periódicos

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