Share This Article:

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

DOI: 10.4236/ojmc.2013.31002    2,864 Downloads   6,402 Views   Citations

ABSTRACT

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

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.

References

[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

  
comments powered by Disqus

Copyright © 2018 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.