QSAR studies combined with DFT-calculations and Molecular docking of polyamine-sensitive inhibitors of the NMDA receptor


  • Mohamed Mazigh chemistry department of Faculty of Sciences of Dhar Elmehraz.
  • Charif El’mbarki chemistry department of Faculty of Sciences of Dhar Elmehraz.
  • Hanine Hadni chemistry department of Faculty of Sciences of Dhar Elmehraz.
  • Menana Elhallaoui chemistry department of Faculty of Sciences of Dhar Elmehraz.




A quantitative structure-activity relationship (QSAR) was carried out to analyze inhibitory activity of 35 compounds, new polyamine-sensitive inhibitors of the NMDA receptor, using multiple linear regression (MLR), artificial neural networks (NN), and the molecular descriptors were calculated using DFT method. This study shows that the compounds' activity correlates reasonably well with six selected descriptors by MLR method. The correlation coefficients calculated by MLR and after that by NN, R =0.878 and R =0.978 respectively, are relatively kind to evaluate the proposed quantitative model, and to predict activity for new polyamine-sensitive inhibitors of the NMDA receptor. The test of the performance of the NN model, using a cross-validation method with a leave-one-out procedure (LOO) shows that the predictive power of this model is relevant (R=0.966). The constitutional molecular descriptors (nN and nHBD) have the most significant impact in the formulation of the QSAR model. The molecular docking investigations exploring the influence of the structural differences in the interaction potency demonstrate that the number of N atoms expressed by multiple hydrogen bonds helps the ligand to be fixed to NR2B subtype of NMDA receptor.


- S.F. Traynelis, L.P. Wollmuth, C.J. Mcbain, J. M. Chris, S.M. Frank, M.V. Katie, K.O. Kevin, B.H. Kasper, Y. Hongjie, J.M. Scott, D. Ray, Glutamate receptor ion channels: structure, regulation, and function, Pharmacol. Rev., 2010, 62, 405-496.

- S. Cull-Candy, S. Brickley, M. Farrant, NMDA receptor subunits: diversity, development and disease, Curr.Opin.Neurobiol., 2001, 11,


- S.F. Traynelis, M. Hartley, S.F. Heinemann, Control of proton sensitivity of the NMDA receptor by RNA splicing and polyamines, Sci., 1995, 26, 873-876.

- K. Williams, C. Romano, P.B. Molinoff, Effects of polyamines on the binding of [3H]MK-801 to the N-methyl-D-aspartate receptor: pharmacological evidence for the existence of a polyamine recognition site, Mol.Pharmacol., 1989, 36, 575-581.

- P. Thomas, S. Oliver, N. Daniela, R. Patrick, L.B. Michael, R.N. Christian, New polyamine-sensitive inhibitors of the NMDA receptor: Syntheses and pharmacological evaluation, Eur. Jour. Medici. Chem., 2007, 42, 175-197.

- H. Hadni, M. Mazigh, E. Charif, A. Bouayad, M. Elhallaoui, Molecular Modeling of Antimalarial Agents by 3D-QSAR Study and Molecular Docking of Two Hybrids 4-amino-quinoline-1,3,5-triazine and 4-amino-quinoline-oxalamide Derivatives with the Receptor Protein in Its Both Wild and Mutant Types, Biochem. Res. Int., 2018, 1-15.

- M.J. Frish, G. W. Turcks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, J. J. A. Montgomery, T. Vreven, K. N. Kudin, J. C. Burant, J. M. Millam, S. S. lyengar, J. Tomasi, V. Barone, B. Mennucci, M. Cossi, G. Scalmani, N. Rega, G.A. Petersson, H. Nakatsuji, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, M. Klene, X. Li, J. E. Knox, H. P. Hratchian, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. Stratmann, O. Yazyev, A. J. Austin, R. Cammi, C. Pomelli, J. W. Ochterski, P. Y. Ayala, K. Morokuma, G. A. Voth, P. Salvador, J. J. Dannenberg, V. G. Zakrzewski, S. Dapprich, A. D. Daniels, M. C. Strain, O. Farkas, D. K. Malick, A. D. Rabuck, K. Raghavachari, J. B. Foresman, J. V. Ortiz, Q. Cui, A. G. Baboul, S. Clifford, J. Cioslowski, B. B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R. L. Martin, D. J. Fox, T. Keith, M. A. Al-Laham, C. Y. Peng, A. Nanayakkara, M. Challacombe, P. M. W. Gill, B. Johnson, W. Chen, M. W. Wong, C. Gonzalez, J. Pople A. Revision D.01, Gaussian, Inc., Wallingford, CT, 2004.

- R. Parr, W. Yang, Density Functional Theory of Atoms and Molecules, Oxf. Uni. Pre., New York, 1989.

- A.D. Becke, ‹‹Density-Functional thermochemistry. III. The rol of exact exchange››, J. Chem. Phys., 1993, 98, 5648

- ACD/ChemSketch Version 4.5 for Microsoft Windows User's Guide.

- G. Saeed, G. Saeed, S. Ali, E.N. Heshmatollah, 2D-QSAR study of some 2,5-diaminobenzo-phenone farnesyltransferase inhibitors by different chemometric methods, ex.jour., 2015, 14, 484-495.

- S. Mbarki, M. El Hallaoui, 3D-QSAR models to predict antimoebic activities of the cyclised pyrazolines and 2-(quinolin-8-yloxy) acetohydrazones, res.Jour. Phar. Bio. Chem. Sci., 2014, 5, 73-83.

- S. So, G. Richards, Application of Neural Networks: Quantitative Structure-Activity Relationships of the Derivatives of 2,4-Diamino-5- (substituted-benzyl) pyrimidines as DHFR Inhibitors. J. Med. Chem., 1992, 35, 3201-3207.

- H. Hadni, M. Mazigh, M. Elhallaoui, QSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors, Mediterr. J. Chem., 2019, 8, 84-93.

- G. Morris, R. Huey, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, J. Comput. Chem., 2009, 30, 2785-2791.

- Discovery Studio Visualizer v4.1.0.14169 Copyright © 2005-14, Accelrys Software Inc.

- J. Rachline, F. Perin-Dureau, A.L. Goff, J. Neyton, P. Paoletti, The Micromolar Zinc-Binding Domain on the NMDA Receptor Subunit NR2B, Jour. Neu., 2005, 12, 308-317.

- R.N. Rama, Molecular Modeling: A Powerful Tool for Drug Design and Molecular Docking, Res., 2004, 9, 51-60.






Computational Chemistry