Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artificial Neural Network
Omar Deeb, Mohammad Jawabreh
DOI: 10.4236/aces.2012.21010   PDF    HTML     5,847 Downloads   9,850 Views   Citations


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.

Share and Cite:

O. Deeb and M. Jawabreh, "Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artificial Neural Network," Advances in Chemical Engineering and Science, Vol. 2 No. 1, 2012, pp. 82-100. doi: 10.4236/aces.2012.21010.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] N. E. Kohl, E. A. Emini, W. A. Schleif, L. J. Davis, J. C. Heimbach, R. A. Dixon, E.M. Scolnick, I. S. Sigal, “Active Human Immunodeficiency Virus Protease is Required for Viral Infectivity,” Proceedings of the National Academy of Sciences, Vol. 85, No. 13, 1988, pp. 4686- 4690. doi:10.1073/pnas.85.13.4686
[2] T. J. McQuade, A. G. Tomasselli, L. Liu, V. Karacostas, B. Moss, T. K. Sawyer, R. L. Heinrikson and W. G. Tarpley, “A Synthetic HIV-1 Protease Inhibitor with Antiviral Activity Arrests HIV-Like Particle Maturation,” Science, Vol. 247, No. 4941, 1990, pp. 454-456. doi:10.1126/science.2405486
[3] D. R. Davies. “The Structure and Function of the Aspartic Proteinases,” Annual Review of Biophysics and Biophysical Chemistry, Vol. 19, No. 1, 1990, pp. 189-215. doi:10.1146/annurev.bb.19.060190.001201
[4] A. Wlodawer and J. W. Erickson, “Structure-Based Inhibitors of HIV-1 Protease,” Annual Review of Biochemistry, Vol. 62, No. 1, 1993, pp. 543- 585. doi:10.1146/annurev.bi.62.070193.002551
[5] M. Fernandez and J. Caballero, “Modeling of Activity of Cyclic Urea HIV-1 Protease Inhibitors Using RegulariZed-Artificial Neural Networks,” Bioorganic & Medicinal Chemistry, Vol. 14, No. 1, 2006, pp. 280-294. doi:10.1016/j.bmc.2005.08.022
[6] 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
[7] O. Aruksakunwong, S. Promsri, K. Wittayanaraku, P. Ni- mmanpipug, V. S. Lee, A. Wijitkosoom, P. Sompornpisut and S. Hannongbua, “Current Development on HIV-1 Protease Inhibitors,” Current Computer-Aided Drug Design, Vol. 3, No. 3, 2007, pp. 201-213. doi:10.2174/157340907781695431
[8] D. C. Montgomery and E. A. Peck, “Introduction to Linear Regression Analysis,” Wiley, New York, 1982.
[9] G. Schneider and P. Wrede, “Artificial Neural Networks for Computer-Based Molecular Design,” Progress in Biophysics and Molecular Biology, Vol. 70, No. 3, 1998, pp. 175-222. doi:10.1016/S0079-6107(98)00026-1
[10] P. J. Gemperline, J. R. Long and G. Gregoriou, “Nonlinear Multivariate Calibration Using Principal Components Regression and Artificial Neural Networks,” Analytical Chemistry, Vol. 63, No. 20, 1991, pp. 2313-2323. doi:10.1021/ac00020a022
[11] R. Vendrame, R. S. Braga, Y. Takahata and D. S. Galvao, “Structure-Activity Relationship Studies of Carcinogenic Activity of Polycyclic Aromatic Hydrocarbons Using Calculated Molecular Descriptors with Principal Component Analysis and Neural Network Methods,” Journal of Chemical Information and Modeling, Vol. 39, No. 6, 1999, pp. 1094-1104. doi:10.1021/ci990326v
[12] B. Hemmateenejad, M. Akhond, R. Miri and M. Shamsipur, “Genetic Algorithm Applied to the Selection of Factors in Principle Component-Artificial Neural Networks: Application to QSAR Study of Calcium Channel Antagonist Activity of 1, 4-Dihydropyridines (Nifedipine Analogous),” Journal of Chemical Information and Modeling, Vol. 43, No. 4, 2003, pp. 1328-1334. doi:10.1021/ci025661p
[13] O. Deeb and B. Hemmateenejad, “ANN-QSAR Model of Drug-Binding to Human Serum Albumin,” Chemical Biology & Drug Design, Vol. 70, No. 1, 2007, pp. 19-29. doi:10.1111/j.1747-0285.2007.00528.x
[14] B. Hemmateenejad, M. A. Safarpour, R. Miri and N. Nesari, “Toward an Optimal Procedure for PC-ANN Model Building: Prediction of the Carcinogenic Activity of a Large Set of Drugs,” Journal of Chemical Information and Modeling, Vol. 45, No. 1, 2005, pp. 190-199. doi:10.1021/ci049766z
[15] G. Ramírez-Galicia, R. Gardu?o-Juárez, O. Deeb and B. Hemmateenejad, “PCR-ANN and RTO Approach to Lopioid Receptor-Binding Affinity. Pooling Data from Different Sources,” Chemical Biology & Drug Design, Vol. 71, No. 3, 2008, pp. 260-270. doi:10.1111/j.1747-0285.2008.00626.x
[16] V. M. Khedkar P. K., Ambre, J. Verma, M. S. Shaikh, R. R. S. Pissurlenkar and E. C. Coutinho, “Molecular Docking and 3D-QSAR Studies of HIV-1 Protease Inhibitors,” Journal of Molecular Modeling, Vol. 16, No. 7, 2010, pp. 1251-1268. doi:10.1007/s00894-009-0636-5
[17] O. Deeb and M. Goodarzi, “Exploring QSARs for Inhibitory Activity of Non-Peptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVM,” Chemical Biology & Drug Design, Vol. 75, No. 5, 2010, pp. 506-514. doi:10.1111/j.1747-0285.2010.00953.x
[18] A. Speranta, C. Bologa and M. L. Flonta, “Quantitative Structure-Activity Relationship by CoMFA for Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives on Wild Type and Mutant HIV-1 Protease,” Journal of Molecular Modeling, Vol. 11, No. 2, 2005, pp. 105-115. doi:10.1007/s00894-004-0226-5
[19] O. Deeb and M. Drabh, “Exploring QSARs of Some Analgesic Compounds by PC-ANN,” Chemical Biology & Drug Design, Vol. 76, No. 3, 2010, pp. 255-262. doi:10.1111/j.1747-0285.2010.01004.x
[20] P. V. Khadikar, O. Deeb, A. Jaber, J. Singh, V. K. Agrawal, S. Singh and M. Lakhwani, “Development of Quantitative Structure-Activity Relationship for a Set of Carbonic Anhydrase Inhibitors: Use of Quantum and Chemical Descriptors,” Letters in Drug Design & Discovery, Vol. 3, No. 9, 2006, pp. 622-635. doi:10.2174/157018006778341138
[21] O. Deeb, B. Hemmateenejad, A. Jaber, R. Garduno-Juarez and R. Miri, “Effect of the Electronic and Physicochemical Parameters on the Carcinogenesis Activity of Some Sulfa Drugs Using QSAR Analysis Based on Genetic-MLR and Genetic PLS,” Chemosphere, Vol. 67, No. 11, 2007, pp. 2122-2130. doi:10.1016/j.chemosphere.2006.12.098
[22] O. Deeb, K. M. Youssef and B. Hemmateenejad, “QSAR of Novel Hydroxyphenylureas as Antioxidant Agents,” QSAR and Combinatorial Sciences, Vol. 27, No. 4, 2008, pp. 417-424. doi:10.1002/qsar.200730023
[23] O. Deeb and M. Goodarzi, “Exploring QSARs for Inhibitory Activity of Nonpeptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVM,” Chemical Biology and Drug Design, Vol. 75, No. 5, 2010, pp. 506-514. doi:10.1111/j.1747-0285.2010.00953.x
[24] O. Deeb, “Correlation Ranking and Stepwise Regression Procedures in PC-ANN Modeling and Application to Predict the Toxic Activity and HSA Binding Affinity,” Chemometrics and Intelegent Laboratory Systems, Vol. 104, No. 2, 2010, pp. 181-194. doi:10.1016/j.chemolab.2010.08.007
[25] A. Golbraikh and A. Tropsha, “Beware of q2!” Journal of Molecular Graphics and Modelling, Vol. 20, No. 4, 2002, pp. 269-276. doi:10.1016/S1093-3263(01)00123-1
[26] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, Vol. 323, 1986, pp. 33-536. doi:10.1038/323533a0
[27] H. Martens and T. Naes, “Multivariate Calibration,” John Wiley, Chichester, 1989.
[28] E. P. P. A. Derks and L. M. C. Buydens, “Aspects of Network Training and Validation on Noisy Data: Part 1. Training Aspects,” Chemometrics and Intelligent Laboratory Systems, Vol. 41, No. 2, 1998, pp. 171-184. doi:10.1016/S0169-7439(98)00053-7

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.