QSAR study on the antinociceptive activity of some morphinans.
Quantitative structure-activity relationship studies were performed to describe and predict the antinociceptive activity of 31 morphinan derivatives reported by the US Drug Evaluation Committee in 2005 and 2006. From these, three data sets were constructed and several models were calculated following the multiple linear regression and Leave-One-Out Cross-Validation (LOO-CV) tests. In general, these models achieved good descriptive power (approximately 92%) as well as predictive power (approximately 76%), but were unable to predict an external validation set of morphinan derivatives. When artificial neural networks were applied to these models, an improvement of the predictive and external validation values was obtained. It was observed that the results of the NN models are significantly better that those obtained by multiple linear regression. In spite that the problem under investigation can be handled adequately by a linear model, a neural network does bring slight improvements in the predictive power.
Ramírez-Galicia
Guillermo
G
Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, PO Box 48-3, 62250 Cuernavaca, Morelos, Mexico. grg@fis.unam.mx
Garduño-Juárez
Ramón
R
Hemmateenejad
Bahram
B
Deeb
Omar
O
Deciga-Campos
Myrna
M
Moctezuma-Eugenio
Juan Carlos
JC
D000700:Analgesics / Q000737:chemistry / Q000378:metabolism; D008968:Molecular Conformation; D015394:Molecular Structure; D009019:Morphinans / Q000737:chemistry / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results
D000700:Analgesics; D009019:Morphinans