Skip to main content
SpringerLink
Log in

Predictive QSAR models of 3-acylamino-2-aminopropionic acid derivatives as partial agonists of the glycine site on the NMDA receptor

  • Original Research
  • Published:
Medicinal Chemistry Research Aims and scope Submit manuscript

Abstract

A quantitative structure–activity relationship (QSAR) model has been carried out for a series of 3-acylamino-2-aminopropionic acid derivatives with high affinities in a binding assay for the glycine site. The replacement method (RM) and stepwise-multiple linear regression (Stepwise-MLR) strategies are used as feature selection (descriptor selection). The model of the relationship between selected molecular descriptors and pK i data has been achieved by linear (multiple linear regression, MLR) and nonlinear (support vector machine, SVM) methods. Leave-one-out cross-validation and external-validation were carried out with the aim for evaluating the predictive capability of the models. The squared correlation coefficients of experimental versus predicted activities for the test set obtained by MLR and SVM models using RM feature selection are 0.463 and 0.655, respectively. Our best QSAR model illustrates the importance of an adequate distribution of atomic properties represented in 3D frames and reveals the mass and van der Waals volumes as the most influencing atomic properties in the structures of the 3-acylamino-2-aminopropionic acid derivatives.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abreu RMV, Ferreira ICFR, Queiroz MJRP (2009) QSAR model for predicting radical scavenging activity of di(hetero)arylamines derivatives of benzo[b]thiophenes. Eur J Med Chem 44:1952–1958

    Article  PubMed  CAS  Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  Google Scholar 

  • Balsamini C, Bedini A, Diamantini G, Spadoni G, Tontini A, Tarzia G (1998) (E)-3-(2-(N-phenylcarbamoyl)vinyl)pyrrole-2-carboxylic acid derivatives. A novel class of glycine site antagonists. J Med Chem 41:808–820

    Article  PubMed  CAS  Google Scholar 

  • Bare TM (1998) Pyridazino[4,5-b]quinolinediones: novel glycine/N-methyl-d-aspartate antagonists for the treatment of stroke. J Heterocycl Chem 35:1171–1186

    Article  CAS  Google Scholar 

  • Brereton RG (2007) Applied chemometrics for scientists. Wiley, New York

    Book  Google Scholar 

  • Buchstaller HP, Siebert CD, Steinmetz R, Frank I, Berger ML, Gottschlich R, Leibrock J, Krug M, Steinhilber D, Noe CR (2006) Synthesis of thieno[2,3-b]pyridinones acting as cytoprotectants and as inhibitors of [3H]glycine binding to the N-methyl-d-aspartate (NMDA) receptor. J Med Chem 49:864–871

    Article  PubMed  CAS  Google Scholar 

  • Buyukbingol E, Sisman A, Akyildiz M, Alparslan FN, Adejare A (2007) Adaptive neuro-fuzzy inference system (ANFIS): a new approach to predictive modeling in QSAR applications: a study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists. Bioorg Med Chem 15:4265–4282

    Article  PubMed  CAS  Google Scholar 

  • Carling RW, Leeson PD, Moore KW, Moyes CR, Duncton M, Hudson ML, Baker R, Foster AC, Grimwood S, Kemp JA, Marshall GR, Tricklebank MD, Saywell KL (1997) 4-Substituted-3-phenylquinolin-2(1H)-ones: acidic and nonacidic Glycine site N-Methyl-d-aspartate antagonists with in vivo activity. J Med Chem 40:754–765

    Article  PubMed  CAS  Google Scholar 

  • Chatterton JE, Awobuluyi M, Premkumar LS, Takahashi H, Talantova M, Shin Y, Cui J, Tu S, Sevarino KA, Nakanishi N, Tong G, Lipton SA, Zhang D (2002) Excitatory glycine receptors containing the NR3 family of NMDA receptor subunits. Nature 415:793–798

    PubMed  CAS  Google Scholar 

  • Cheng ZJ, Zhang YT, Zhang WJ (2009a) QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors by using PSO-SVM approach. Med Chem Res. doi:10.1007/s00044-009-9272-y

  • Cheng ZJ, Zhang YT, Zhou CH, Zhang WJ, Gao SB (2009b) Classification of 5-HT1A receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM. Int J Mol Sci 10:3316–3337

    Article  PubMed  CAS  Google Scholar 

  • Cherqaoui D, Esseffar M, Villemin D, Cense JM, Chastrette M, Zakarya D (1998) Structure-musk odour relationship studies of tetralin and indan compounds using neural networks. New J Chem 22:839–843

    Article  CAS  Google Scholar 

  • Chihchung C, Chihjen L (2009) LIBSVM-a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm

  • Clementi S, Wold S (1995) How to choose the proper statistical method. In: van de Waterbeemd H (ed) Chemometrics methods in molecular design. VCH, Weinheim, pp 319–338

    Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Google Scholar 

  • Dougherty ER, Barrera J, Brun M, Kim S, Cesar RM, Chen Y, Bittner M, Trent JM (2002) Inference from clustering with application to gene-expression microarrays. J Comput Biol 9:105–126

    Article  PubMed  CAS  Google Scholar 

  • Doytchinova IA, Walshe V, Borrow P, Flower DR (2005) Towards the chemometric dissection of peptide—HLA-A*0201 binding affinity: comparison of local and global QSAR models. J Comput Aid Mol Des 15:203–212

    Article  Google Scholar 

  • Duchowicz PR, Fernández FM, Castro EA (2006) Alternative algorithm for the search of an optimal set of descriptors in QSAR-QSPR studies. MATCH Commun Math Comput Chem 55:179–192

    CAS  Google Scholar 

  • Duchowicz PR, González MP, Helguera AM, Cordeiro MDNS, Castro EA (2007) Application of the replacement method as novel variable selection in QSPR. 2. Soil sorption coefficients. Chemom Intell Lab Syst 88:197–203

    Article  CAS  Google Scholar 

  • González MP, Caballero J, Camba AT, Helguerab AM, Fernándezc M (2006a) Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches. Bioorgan Med Chem 14:200–213

    Article  Google Scholar 

  • González MP, Puente M, Fall Y, Gómez G (2006b) In silico studies using radial distribution function approach for predicting affinity of 1α,25-dihydroxyvitamin D3 analogues for Vitamin D receptor. Steroids 71:510–527

    Article  PubMed  Google Scholar 

  • Goodarzi M, Freitas MP (2008) Augmented three-mode MIA-QSAR modeling for a series of anti-HIV-1 compounds. QSAR Comb Sci 27:1092–1098

    Article  CAS  Google Scholar 

  • Huettner JE (1991) Competitive antagonism of glycine at the N-methyl-d-aspartate (NMDA) receptor. Biochem Pharmacol 41:9–16

    Article  PubMed  CAS  Google Scholar 

  • Jansen M, Dannhardt G (2003) Antagonists and agonists at the glycine site of the NMDA receptor for therapeutic interventions. Eur J Med Chem 38:661–670

    Article  PubMed  CAS  Google Scholar 

  • Johnson JW, Ascher P (1987) Glycine potentiates the NMDA response in cultured mouse brain neurons. Nature 325:529–531

    Article  PubMed  CAS  Google Scholar 

  • Kemp JA, McKernan RM (2002) NMDA receptor pathways as drug targets. Nat Neurosci 5:1039–1042

    Article  PubMed  CAS  Google Scholar 

  • Kleckner NW, Dingledine R (1988) Requirements for glycine in activation of NMDA receptors expressed in Xenopus oocytes. Science 241:835–837

    Article  PubMed  CAS  Google Scholar 

  • Kubinyi H (1994a) Variable selection in QSAR studies. I. An evolutionary algorithm. Quant Struct Act Relatsh 13:285–294

    CAS  Google Scholar 

  • Kubinyi H (1994b) Variable selection in QSAR studies. II. A highly efficient combination of systematic search and evolution. Quant Struct Act Relatsh 13:393–401

    CAS  Google Scholar 

  • Kulagowski JJ (1996) Glycine-site NMDA receptor antagonists: an update. Exp Opin Ther Pat 6:1069–1079

    Article  CAS  Google Scholar 

  • Kulagowski JJ, Leeson PD (1995) Glycine-site NMDA receptor antagonists. Exp Opin Ther Pat 5:1061–1075

    Article  CAS  Google Scholar 

  • Labrie V, Roder JC (2010) The involvement of the NMDA receptor d-serine/glycine site in the pathophysiology and treatment of schizophrenia. Neurosci Biobehav Rev 34:351–372

    Article  PubMed  CAS  Google Scholar 

  • Leeson PD, Iversen LL (1994) The glycine site on the NMDA receptor: structure–activity relationship and therapeutic potential. J Med Chem 37:4053–4067

    Article  PubMed  CAS  Google Scholar 

  • Li XM, Zhao M, Tang YR, Wang C, Zhang ZD, Peng SQ (2008) N-[2-(5,5-Dimethyl-1,3-dioxane-2-yl)ethyl]amino acids: their synthesis, anti-inflammatory evaluation and QSAR analysis. Eur J Med Chem 43:8–18

    Article  PubMed  CAS  Google Scholar 

  • Liu HX, Zhang RS, Yao XJ, Liu MC, Hul ZD, Fan BT (2004) QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines. J Comput Aid Mol Des 18:389–399

    Article  CAS  Google Scholar 

  • Mercader AG, Duchowicz PR, Fernández FM, Castro EA (2008) Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories. Chemom Intell Lab Syst 92:138–144

    Article  CAS  Google Scholar 

  • Newcomer JW, Krystal JH (2001) NMDA receptor regulation of memory and behavior in humans. Hippocampus 11:529–542

    Article  PubMed  CAS  Google Scholar 

  • Panek JJ, Jezierska A, Vračko M (2005) Kohonen network study of aromatic compounds based on electronic and nonelectronic structure descriptors. J Chem Inf Model 45:264–272

    Article  PubMed  CAS  Google Scholar 

  • Schneider CI, Urwyler S (1992) Biochemical and thermodynamic aspects of the binding of [3H]glycine to its strychnine-insensitive recognition site associated with the N-methyl-d-aspartate receptor complex. Biochem Pharmacol 43:1693–1699

    Article  PubMed  CAS  Google Scholar 

  • Sia HZ, Zhang KJ, Hua ZD, Fand BT (2007) QSAR model for prediction capacity factor of molecular imprinting polymer based on gene expression programming. QSAR Comb Sci 26:41–50

    Article  Google Scholar 

  • Sills MA, Fagg G, Pozza M, Angst C, Brundish DE, Hurt SD, Wilusz EJ, Williams M (1991) [3H]CGP39653: a new N-methyl-d-aspartate antagonist radioligand with low nanomolar affinity in rat brain. Eur J Pharmacol 192:19–24

    Article  PubMed  CAS  Google Scholar 

  • Tedesco G, Feriani A, Mor M (2000) Statistical analysis on a series of glycine antagonists. II Farmaco 55:194–196

    Article  CAS  Google Scholar 

  • Thomson AM (1990) Glycine is a coagonist at the NMDA receptor/ion channel complex. Progr Neurobiol 35:53–74

    Article  CAS  Google Scholar 

  • Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim (Germany)

    Book  Google Scholar 

  • Urwyler S, Laurie D, Lowe DA, Meier CL, Muller W (1996) Biphenyl-derivatives of 2-amino-7-phosphonoheptanoic acid, a novel class of potent competitive N-methyl-d-aspartate receptor antagonists. I. Pharmacological characterization in vitro. Neuropharmacology 35:643–654

    PubMed  CAS  Google Scholar 

  • Urwyler S, Floersheim P, Roy BL, Koller M (2009) Drug design, in vitro pharmacology, and structure–activity relationships of 3-acylamino-2-aminopropionic acid derivatives, a novel class of partial Agonists at the glycine site on the N-Methyl-d-aspartate (NMDA) receptor complex. J Med Chem 52:5093–5107

    Article  PubMed  CAS  Google Scholar 

  • Yu YJ, Su RX, Wang LB, Qi W, He ZM (2009) Comparative QSAR modeling of antitumor activity of ARC-111 analogues using stepwise MLR, PLS, and ANN techniques. Med Chem Res. doi:10.1007/s00044-009-9266-9

  • Yuan YN, Zhang RS, Hu RJ, Ruan XF (2009) Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression. Eur J Med Chem 44:25–34

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Applied Chemistry, China West Normal University, Nanchong, 637002, Sichuan, China

    Zhengjun Cheng, Yuntao Zhang & Weizhong Fu

Authors
  1. Zhengjun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuntao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weizhong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuntao Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cheng, Z., Zhang, Y. & Fu, W. Predictive QSAR models of 3-acylamino-2-aminopropionic acid derivatives as partial agonists of the glycine site on the NMDA receptor. Med Chem Res 20, 1235–1246 (2011). https://doi.org/10.1007/s00044-010-9464-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00044-010-9464-5

Keywords

Search

Navigation