Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas


Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

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R. Mouhibi, M. Zahouily, K. Akri and N. Hanafi, "Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas," Open Journal of Medicinal Chemistry, Vol. 3 No. 1, 2013, pp. 7-15. doi: 10.4236/ojmc.2013.31002.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Y. Zhuo, R. Kong, X.-J. Cong, W.-Z. Chen and C.-X. Wang, “Three-Dimensional QSAR Analyses of 1,3,4-Trisubstituted Pyrrolidine-Based CCR5 Receptor Inhibitors,” European Journal of Medicinal Chemistry, Vol. 43, No. 12, 2008, pp. 2724-2734. doi:10.1016/j.ejmech.2008.01.040
[2] I. P. Ribeiro, C. G. Schrago, E. A. Soares, A. Pissinatti, H. N. Seuanez, C. A. M. Russo, A. Tanuri and M. A. Soares, “CCR5 Chemokine Receptor Gene Evolution in New World Monkeys (Platyrrhini, Primates): Implication on Resistance to Lentiviruses,” Infection Genetics and Evolution, Vol. 5, No. 3, 2005, pp. 271-280. doi:10.1016/j.meegid.2004.07.009
[3] J. Ernst, R. Dahl, C. Lum, L. Sebo, J. Urban, S.G. Miller and J. Lundstr?, “Anti-HIV-1 Entry Optimization of Novel Imidazopiperidine-Tropane CCR5 Antagonists,” Bioorganic & Medicinal Chemistry Letters, Vol. 18, No. 4, 2008, pp. 1498-1501.
[4] F. J. Prado-Prado, X. García-Mera and H. González-Díaz, “Multi-Target Spectral Moment QSAR versus ANN for Antiparasitic Drugs against Different Parasite,” Bioorganic & Medicinal Chemistry, Vol. 18, No. 6, 2010, pp. 2225-2231. doi:10.1016/j.bmc.2010.01.068
[5] M. Hossein Fatemi and S. Gharaghani, “A Novel QSAR Model for Prediction of Apoptosis-Inducing Activity of 4-Aryl-4-H-Chromenes Based on Support Vector Machine,” Bioorganic & Medicinal Chemistry, Vol. 15, No. 24, 2007, pp. 7746-7754. doi:10.1016/j.bmc.2007.08.057
[6] Y. Yuan, R. Zhang and R. Hu, X. Ruan, “Prediction of CCR5 Receptor Binding Affinity of Substituted 1-(3,3Diphenylpropyl)-Piperidinyl Amides and Ureas Based on the Heuristic Method, Support Vector Machine and Projection Pursuit Regression,” European Journal of Medicinal Chemistry, Vol. 44, No. 1, 2009, pp. 25-34. doi:10.1016/j.ejmech.2008.03.004
[7] J. T. Leinard and K. Roy, “Comparative QSAR Modeling of CCR5 Receptor Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas,” Bioorganic & Medicinal Chemistry Letters, Vol. 16, No. 17, 2006, pp. 4467-4474.
[8] H. Bazoui, M. Zahouily, S. Sebti, S. Boulaajaj and D. Zakarya, “Structure-Cytotoxicity Relationships for a Series of HEPT Derivatives,” Journal of Molecular Modeling, Vol. 8, No. 1, 2002, pp. 1-7. doi:10.1007/s00894-001-0054-9
[9] M. Zahouily, A. Rhihil, H. Bazoui, S. Sebti and D. Zakarya, “Structure-Toxicity Relationships Study of a Series of Organophosphorus Insecticides,” Journal of Molecular Modeling, Vol. 8, No. 5, 2002, 168-172. doi:10.1007/s00894-002-0074-0
[10] M. Zahouily, M. Lazar, A. Elmakssoudi, J. Rakik, S. Elaychi and A. Rayadh, “QSAR for Anti-Malarial Activity of 2-Aziridinyl and 2,3-Bis(Aziridinyl)-1,4-Naphthoquinonyl Sulfonate and Acylate Derivatives,” Journal of Molecular Modeling, Vol. 12, No. 4, 2006, pp. 398-405. doi:10.1007/s00894-005-0059-x
[11] M. Zahouily, A. Rayadh, M. Aadil and D. Zakarya, “Quantitative Structure-Diastereoselectivity Relationships for Arylsulfoxide Derivatives in Radical Chemistry,” Journal of Molecular Modeling, Vol. 9, No. 4, 2003, pp. 242-247. doi:10.1007/s00894-003-0136-y
[12] J. S. Song, T. Moon, K. D. Nam, J. K. Lee, H. G. Hahn, E. J. Choi and C. N. Yoon, “Quantitative Structural-Activity Relationship (QSAR) Study for Fungicidal Activities of Thiazoline Derivatives against Rice Blast,” Bioorganic & Medicinal Chemistry Letters, Vol. 18, No. 6, 2008, pp. 2133-2142.
[13] C. Bergmeir and J. M. Benítez, “On the Use of CrossValidation for Time Series Predictor Evaluation,” Information Sciences, Vol. 191, 2012, pp 192-213.
[14] Y. Liu, Z. Ke, J. Cui, W. Chen, L. Ma and B. Wang, “Synthesis, Inhibitory Activities, and QSAR Study of Xanthone Derivatives as Alpha-Glucosidase Inhibitors,” Bioorganic & Medicinal Chemistry, Vol. 16, No. 15, 2008, pp. 7185-7192.
[15] C. N. Alves, J. C. Pinheiro, A. J. Camargo, M. M. C. Ferreira, R. A. F. Romero and A. B. F. Da Silva, “A Multiple Linear Regression and Partial Least Squares Study of Flavonoid Compounds with Anti-HIV,” Journal of Molecular Structure, Vol. 541, No. 1, 2001, pp. 81-88.
[16] M. Jalali-Heravi, M. Asadollahi-Baboli and P. Shahbazikhah, “QSAR Study of Heparanase Inhibitors Activity Using Artificial Neural Networks and Levenberg Marquardt Algorithm,” European Journal of Medicinal Chemistry, Vol. 43, No. 3, 2008, pp. 548-556.
[17] K. De, C. Sengupta and K. Roy, “QSAR Modeling of Globulin Binding Affinity of corticosteroids Using AM1 Calculations,” Bioorganic & Medicinal Chemistry, Vol. 12, No. 12, 2004, pp. 3323-3332.
[18] K. Roy and J. T. Leonard, “QSAR by LFER Model of Cytotoxicity Data of Anti-HIV 5-Phenyl-1-Phenylamino1H-Imidazole Derivatives Using Principal Component Factor Analysis and Genetic Function Approximation,” Bioorganic & Medicinal Chemistry, Vol. 13, No. 8, 2005, pp. 2967-2973. doi:10.1016/j.bmc.2005.02.003
[19] A. Speck-Planche, V. V. Kleandrova, F. Luan and M. N. D. S Cordeiro, “Rational Drug Design for Anti-Cancer Chemotherapy: Multi-Target QSAR Models for the in Silico Discovery of Anti-Colorectal Cancer Agents,” Bioorganic & Medicinal Chemistry, Vol. 20, No. 15, 2012, pp. 4848-4855doi:10.1016/j.bmc.2012.05.071
[20] K. Dincer, S. Tasdemir, S. Baskaya and B. Z. Uysal, “Modeling of the Effects of Length to Diameter Ratio and Nozzle Number on the Performance of Counter Flow Ranque-Hilsch Vortex Tubes Using Artificial Neural Networks,” Applied Thermal Engineering, Vol. 28, No. 17-18, 2008, pp. 2380-2390. doi:10.1016/j.applthermaleng.2008.01.016
[21] S. Satish and Y. P. Setty, “Modeling of a Continuous Fluidized Bed Dryer Using Artificial Neural Networks,” Heat and Mass Transfer, Vol. 32, No. 3-4, 2005, pp. 539-547. doi:10.1016/j.eswa.2008.01.042
[22] B. Abbasi, “A Neural Network Applied to Estimate Process Capability of Non-Normal Processes,” Expert Systems with Applications, Vol. 36, No. 2, 2009, pp. 3093-3100. doi:10.1016/j.ejmech.2008.02.041
[23] C. Hansch and R. P. Verma, “A QSAR Study for the Cytotoxic Activities of Taxoids against Macrophage (MΦ)-Like Cells,” European Journal of Medicinal Chemistry, Vol. 44, No. 1, 2009, pp. 274-279.
[24] M. Zahouily, M. Lazar, M. Boumarzouk, R. Mouhibi, M. Nohair and M. A. Bahlaoui, “A Quantitative Structure-Activity Relationship Model,” Chemical Product and Process Modelling, Vol. 3, No. 1, 2008, pp. 1-8.
[25] W. L. Gore, “Statistical Methods for Chemical Experimentation,” Interscience, New York, 1952, p. 141.
[26] P. P. Roy, J. T. Leonard, K. Roy, “Exploring the Impact of Size of Training Sets for the Development of Predictive QSAR Models,” Chemometrics and Intelligent Laboratory Systems, Vol. 90, No. 1, 2008, pp. 31-42. doi:10.1016/j.chemolab.2007.07.004
[27] F. Zheng, E. Bayram, S. P. Sumithran, J. T. Ayers, C. Zhan, J. D. Schmitt and L. P. Dwoskin, “QSAR Modeling of Monoand Bis-Quaternary Ammonium Salts That Act as Antagonists at Neuronal Nicotinic Acetylcholine Receptors Mediating Dopamine Release,” Bioorganic & Medicinal Chemistry, Vol. 14, No. 9, 2006, pp. 3017-3037. doi:10.1016/j.bmc.2005.12.036

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