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Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks by Michael Fernández; Julio Caballero is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K i ) and inhibition of HIV replication (IC 90 ) 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 K i 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 ðQ 2 LOO 1⁄4 0:703 vs: 0:510Þ. On the other hand, IC 90 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; Q 2 LOO 1⁄4 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-mem

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Author

Michael Fernández; Julio Caballero

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0968-0896)

Published

2006

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)