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• W2011886501 abstract "Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (Ki) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of Ki overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (QLOO2=0.703vs.0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; QLOO2=0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models." @default.
• W2011886501 created "2016-06-24" @default.
• W2011886501 creator A5029378374 @default.
• W2011886501 creator A5086197164 @default.
• W2011886501 date "2006-01-01" @default.
• W2011886501 modified "2023-09-26" @default.
• W2011886501 title "Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks" @default.
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• W2011886501 doi "https://doi.org/10.1016/j.bmc.2005.08.022" @default.
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