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N. A. Roberts, J. A. Martin, D. Kinchington, A. V. Broadhurst, J. C. Craig, I. B. Duncan, S. A. Galpin, B. K. Handa, J. Kay, A. Kroehn, R. W. Lambert, J. H. Merrett, J. S. Mills, K. E. B. Parkes, S. Redshaw, A. J. Ritchie, D. L. Taylor, G. J. Thomas and P. J. Machin, “Rational Design of Peptide-Based HIV Proteinase Inhibitors,” Science, Vol. 248, No. 4953, 1990, pp. 358-361. doi:10.1126/science.2183354

has been cited by the following article:

• TITLE: Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artificial Neural Network

  AUTHORS: Omar Deeb, Mohammad Jawabreh

  KEYWORDS: QSAR; MLR; PC; ANN; Inhibitory Activity; Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives; HIV-1 Protease

  JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.2 No.1, December 31, 2011

  ABSTRACT: Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.