A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs

Molecular Diversity

Volumn 14, Issue 2, 2010, Pages 225-235

  Afantitis, Antreas ^a,b   Melagraki, Georgia ^c   Sarimveis, Haralambos ^c   Koutentis, Panayiotis A ^d   Igglessi Markopoulou, Olga ^c   Kollias, George ^a  

^a BIOMEDICAL SCIENCES RESEARCH CENTER ALEXANDER FLEMING   (Greece)

^b NOVAMECHANICS LTD   (Cyprus)

^c NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^d UNIVERSITY OF CYPRUS   (Cyprus)

Author keywords

CXCR3; In silico predictions; Molecular modelling; QSAR

Indexed keywords

ANTIINFLAMMATORY AGENT; CHEMOKINE RECEPTOR CXCR3; QUINAZOLINONE DERIVATIVE;

ARTICLE; COMPUTER MODEL; IC 50; KERNEL METHOD; LEAST SQUARES SUPPORT VECTOR MACHINE; MOLECULAR MODEL; PHARMACOPHORE; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RADIAL BASED FUNCTION; RECEPTOR BLOCKING; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VALIDATION STUDY; ALGORITHM; CHEMICAL MODEL; CHEMISTRY; DRUG ANTAGONISM; REGRESSION ANALYSIS; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; INHIBITORY CONCENTRATION 50; LEAST-SQUARES ANALYSIS; LINEAR MODELS; MODELS, CHEMICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; QUINAZOLINONES; RECEPTORS, CXCR3; REPRODUCIBILITY OF RESULTS;

EID: 77954670462     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-009-9163-7     Document Type: Article

References (51)

1. Neote K (2007) Chemokine biology: basic research and clinical application: vol 2: pathophysiology of chemokines (Progress in Inflammation Research). Birkhäuser, Basel
2. Wijtmans M, Verzijl D, Leurs R, de Esch IJ, Smit MJ (2008) Towards small-molecule CXCR3 ligands with clinical potential. ChemMedChem 3:861-872. doi:10.1002/cmdc.200700365
3. Cole AG, Stroke IL, Brescia MR, Simhadri S, Zhang JJ, Hussain Z et al (2006) Identification and initial evaluation of 4-N-aryl- [1,4]diazepane ureas as potent CXCR3 antagonists. Bioorg Med Chem Lett 16:200-203. doi:10.1016/j.bmcl.2005.09.020 (Pubitemid 41625670)
4. Johnson M, Li AR, Liu J, Fu Z, Zhu L,Miao S et al (2007) Discovery and optimization of a series of quinazolinone-derived antagonists of CXCR3. Bioorg Med Chem Lett 17:3339-3343. doi:10. 1016/j.bmcl.2007.03.106
5. Du X, Chen X, Mihalic JT, Deignan J, Duquette J, Li AR et al (2008) Design and optimization of imidazole derivatives as potent CXCR3 antagonists. Bioorg Med Chem Lett 18:608-613. doi:10. 1016/j.bmcl.2007.11.072
6. Roy K, Mandal AS (2009) Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools. J Enzyme Inhib Med Chem 24:205-223. doi:10.1080/ 14756360802051297
7. Aher YD, Agrawal A, Bharatam PV, Garg P (2007) 3D-QSAR studies of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists. J Mol Model 13:519-529. doi:10.1007/s00894-007-0173-z (Pubitemid 46404035)
8. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques. J Comput Aided Mol Des 20:83-95. doi:10.1007/s10822- 006-9038-9042
9. Nair PC, Srikanth K, Sobhia ME (2008) QSAR studies on CCR2 antagonistswith chiral sensitive hologram descriptors. BioorgMed Chem Lett 18:1323-1330. doi:10.1016/j.bmcl.2008.01.023 (Pubitemid 351226543)
10. Srikanth K, Nair PC, Sobhia ME (2008) Probing the structural and topological requirements for CCR2 antagonism: holographic QSAR for indolopiperidine derivatives. Bioorg Med Chem Lett 18:1450-1456. doi:10.1016/j.bmcl.2007.12.072 (Pubitemid 351226528)
11. Khlebnikov AI, Schepetkin IA, Quinn MT (2006) Quantitative structure-activity relationships for small non-peptide antagonists of CXCR2: indirect 3D approach using the frontal polygon method. Bioorg Med Chem 14:352-365. doi:10.1016/j.bmc.2005.08.026 (Pubitemid 41767595)
12. Bhonsle JB, Wang Z, Tamamura H, Fujii N, Peiper SC, Trent JO (2005) A simple, automated quasi-4D-QSAR, quasi-multi way PLS approach to develop highly predictive QSAR models for highly flexible CXCR4 inhibitor cyclic pentapeptide ligands using scripted common molecular modeling tools. QSAR Comb Sci 24:620-630. doi:10.1002/qsar.200430912 (Pubitemid 41074330)
13. Afantitis A, Melagraki G, Sarimveis H, Igglessi-Markopoulou O, Kollias G (2009) A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas. Eur J Med Chem 44:877-884. doi:10.1016/j.ejmech. 2008.05.028
14. Todeschini R, Consonni V, Mannhold R (2000) In: Kubinyi H, Timmerman H (eds) Handbook of molecular descriptors. Wiley- VCH, Weinheim
15. Suykens JAK,Van Gestel T,De Brabanter J,De Moor B,Vandewalle J (2002) Least squares support vector machines.World Scientific Pub Co., Singapore
16. Stewart JJP (2007) Optimization of parameters for semiempirical methodsV: modification ofNDDOapproximations and application to 70 elements. J Mol Model 13:1173-1213. doi:10.1007/s00894- 007-0233-234
17. Stewart JJP (2008) Application of the PM6 method to modeling the solid state. J Mol Model 14:499-535. doi:10.1007/s00894-008- 0299-307
18. Puzyn T, Suzuki N, Haranczyk M, Rak J (2008) Calculation of quantum-mechanical descriptors for QSPR at the dft level: is it necessary? J Chem Inf Model 48:1174-1180. doi:10.1021/ci800021p
19. Chem 3D. CambridgeSoft Corporation, 100 CambridgePark Drive Cambridge, MA 02140, USA. http://www.cambridgesoft.com
20. Topix. Epina GmbH, Am Wienerwald 15, 3013 Pressbaum, Austria. http://www.lohninger.com/topix.html
21. MOPAC2007. Stewart Computational Chemisitry (SCC), 15210 Paddington Circle Colorado Springs, CO80921, USA, http:// openmopac.net/home.html
22. ROCS & EON. OpenEye Scientific Software Inc, 9 Bisbee Court, Suite D Santa Fe, NM 87508, USA. http://www.eyesopen.com
23. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137-148. doi:10.2307/1266770
24. Ghosh P, Thanadath M, Bagchi MC (2006) On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials. Mol Divers 10:415-427. doi:10.1007/s11030-006-9018-4 (Pubitemid 46156696)
25. MelagrakiG,Afantitis A, SarimveisH,Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2007) Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening. J Comput Aided Mol Des 21:251-267. doi:10.1007/s10822-007- 9112-9114
26. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-MarkopoulouO(2006)Anovel simpleQSARmodel for the prediction of anti-HIV activity using multiple linear regression analysis. Mol Divers 10:405-414. doi:10.1007/s11030-005- 9012-9022
27. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182. doi:10.1162/ 153244303322753616
28. Hung YH, Liao YS (2008) Applying PCA and fixed size LS-SVM method for large scale classification problems. Inf Technol J 7:890-896. doi:10.3923/itj.2008.890.896
29. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861-874. doi:10.1016/j.patrec.2005.10.010
30. Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13:3494-3504. doi:10.2174/138161207782794257 (Pubitemid 350238939)
31. Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2007) A novel QSPR model for predicting θ (lower critical solution temperature) in polymer solutions using molecular descriptors. J Mol Model 13:55-64. doi:10. 1007/s00894-006-0125-z
32. Golbraikh A, Tropsha A (2002) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol Divers 5:231-243. doi:10.1023/A: 1021372108686
33. Melagraki G, Afantitis A, Sarimveis H, Igglessi-Markopoulou O, Alexandridis A (2006) A novel RBF neural network trainingmethodology to predict toxicity to Vibrio fischeri. Mol Divers 10:213-221. doi:10.1007/s11030-005-9008- y (Pubitemid 44092210)
34. Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2007) Identification of a series of novel derivatives as potentHCVinhibitors by a ligand-based virtual screening optimized procedure. BioorgMed Chem 15:7237-7247. doi:10.1016/j.bmc.2007.08.036 (Pubitemid 47539153)
35. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2008) Development and evaluation of a QSPR model for the prediction of diamagnetic susceptibility. QSAR Comb Sci 27:432-436. doi:10.1002/qsar.200730083
36. Toropov AA, Benfenati E (2008) Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: using rare SMILES attributes to define the applicability domain. Bioorg Med Chem 16:4801-4809. doi:10.1016/j.bmc.2008.03. 048 (Pubitemid 351625898)
37. Todeschini R, Consonni V, Mauri A, Pavan M (2004) Detecting "bad" regression models: multicriteria fitness functions in regression analysis. Anal Chim Acta 515:199-208. doi:10.1016/j.aca. 2003.12.010
38. Afantitis A, MelagrakiG, Sarimveis H,Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) A novel QSAR model for evaluating and predicting the inhibition activity of dipeptidyl aspartyl fluoromethylketones. QSAR Comb Sci 25:928-935. doi:10.1002/ qsar.200530208
39. Jalali-Heravi M, Asadollahi-Baboli M, Shahbazikhah P (2008) QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm. Eur J Med Chem 43:548-556. doi:10.1016/j.ejmech. 2007.04.014 (Pubitemid 351324887)
40. Ertl P, Rohde B, Selzer P (2000) Fast calculation ofmolecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 43:3714-3717. doi:10.1021/jm000942e
41. Patai S (1992) Patai's 1992 guide to the chemistry of functional groups. Wiley, Chichester
42. McQuarrie DA, Simon JD (1997) Physical chemistry: a molecular approach. University Science Books, CA
43. Kier LB (1986) Molecular connectivity in structure-activity analysis (chemometrics series). Wiley, New York
44. Devillers J, Balaban AT (1999) Topological indices and related descriptors inQSAR and QSPAR. Taylor&Francis Inc, NewYork
45. Colombo A, Benfenati E, Karelson M, Maran U (2008) The proposal of architecture for chemical splitting to optimizeQSARmodels for aquatic toxicity. Chemosphere 72:772-780. doi:10.1016/j. chemosphere.2008.03.016
46. Baumann K (2003) Cross-validation as the objective function for variable-selection techniques. Trends Analyt Chem 22:395-406. doi:10.1016/S0165-9936(03)00607-1 (Pubitemid 36694985)
47. Agrafiotis DK, Bandyopadhyay D, Wegner JK, Vlijmen H (2007) Recent advances in chemoinformatics. J Chem Inf Model 47: 1279-1293. doi:10.1021/ci700059g (Pubitemid 47214446)
48. Muegge I, Oloff S (2006) Advances in virtual screening. Drug Discov Today Technol 3:405-411. doi:10.1016/j.ddtec.2006.12.002 (Pubitemid 46164322)
49. Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Kollias G, Igglessi-Markopoulou O (2009) Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors. Mol Divers. doi:10.1007/s11030-009-9115-2
50. Salum LB, Andricopulo AD (2009) Fragment-based QSAR: perspectives in drug design. Mol Divers 2009. doi:10.1007/ s11030-009-9112-9115
51. Guido RV, Oliva G, Andricopulo AD (2008) Virtual screening and its integration with modern drug design technologies. Curr Med Chem 15:37-46. doi:10.2174/092986708783330683 (Pubitemid 351234626)