TY - JOUR
T1 - QSAR study on the antinociceptive activity of some morphinans
AU - Ramírez-Galicia, Guillermo
AU - Garduño-Juárez, Ramón
AU - Hemmateenejad, Bahram
AU - Deeb, Omar
AU - Deciga-Campos, Myrna
AU - Moctezuma-Eugenio, Juan Carlos
PY - 2007/7
Y1 - 2007/7
N2 - 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.
AB - 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.
KW - Antinociceptive activity
KW - Cheminformatics
KW - Hot Plate
KW - Morphinan derivatives
KW - Natural products
KW - Quantitative structure-activity relationship
KW - Structure-based drug design
UR - http://www.scopus.com/inward/record.url?scp=34447308558&partnerID=8YFLogxK
U2 - 10.1111/j.1747-0285.2007.00530.x
DO - 10.1111/j.1747-0285.2007.00530.x
M3 - Artículo
C2 - 17630995
SN - 1747-0277
VL - 70
SP - 53
EP - 64
JO - Chemical Biology and Drug Design
JF - Chemical Biology and Drug Design
IS - 1
ER -