Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

European Journal of Medicinal Chemistry

Volumn 76, Issue , 2014, Pages 352-359

  Nantasenamat, Chanin ^a   Monnor, Teerawat ^a   Worachartcheewan, Apilak ^a   Mandi, Prasit ^a   Isarankura Na Ayudhya, Chartchalerm ^a   Prachayasittikul, Virapong ^a  

^a MAHIDOL UNIVERSITY   (Thailand)

Author keywords

Aldose reductase; Aldose reductase inhibitor; MC MLR; Monte Carlo; QSAR

Indexed keywords

ALDOSE REDUCTASE INHIBITOR; ZOPOLRESTAT; ALDEHYDE REDUCTASE; ENZYME INHIBITOR;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL ACTIVITY; BLOOD PH; CHEMICAL STRUCTURE; COMPARATIVE STUDY; CONTROLLED STUDY; DRUG PENETRATION; DRUG STRUCTURE; HUMAN; HYDROGEN BOND; HYPERSENSITIVITY; IC 50; LIVER TOXICITY; MOLECULAR WEIGHT; MONTE CARLO METHOD; PHYSICAL CHEMISTRY; PREDICTIVE VALUE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RASH; SUPPORT VECTOR MACHINE; ANTAGONISTS AND INHIBITORS; CHEMISTRY;

ALDEHYDE REDUCTASE; ENZYME INHIBITORS; MONTE CARLO METHOD; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINES;

EID: 84896804902     PISSN: 02235234     EISSN: 17683254     Source Type: Journal    
DOI: 10.1016/j.ejmech.2014.02.043     Document Type: Article

References (41)

1. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King Global prevalence of diabetes: estimates for the year 2000 and projections for 2030 Diabetes Care 27 2004 1047 1053
2. J.H. Kinoshita, and C. Nishimura The involvement of aldose reductase in diabetic complications Diabetes Metabolism Reviews 4 1988 323 337
3. Q.Q. Wang, N. Cheng, X.W. Zheng, S.M. Peng, and X.Q. Zou Synthesis of organic nitrates of luteolin as a novel class of potent aldose reductase inhibitors Bioorganic & Medicinal Chemistry 21 2013 4301 4310
4. E. Kotsampasakou, and V.J. Demopoulos Synthesis of derivatives of the keto-pyrrolyl-difluorophenol scaffold: some structural aspects for aldose reductase inhibitory activity and selectivity Bioorganic & Medicinal Chemistry 21 2013 869 873
5. V.R. Rao, P. Muthenna, G. Shankaraiah, C. Akileshwari, K.H. Babu, G. Suresh, K.S. Babu, R.S. Chandra Kumar, K.R. Prasad, P.A. Yadav, J.M. Petrash, G.B. Reddy, and J.M. Rao Synthesis and biological evaluation of new piplartine analogues as potent aldose reductase inhibitors (ARIs) European Journal of Medicinal Chemistry 57 2012 344 361
6. Y. Yang, S. Zhang, B. Wu, M. Ma, X. Chen, X. Qin, M. He, S. Hussain, C. Jing, B. Ma, and C. Zhu An efficient synthesis of quinoxalinone derivatives as potent inhibitors of aldose reductase ChemMedChem 7 2012 823 835
7. X. Chen, Y. Yang, B. Ma, S. Zhang, M. He, D. Gui, S. Hussain, C. Jing, C. Zhu, Q. Yu, and Y. Liu Design and synthesis of potent and selective aldose reductase inhibitors based on pyridylthiadiazine scaffold European Journal of Medicinal Chemistry 46 2011 1536 1544
8. P. Mandi, C. Nantasenamat, K. Srungboonmee, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul QSAR study of anti-prion activity of 2-aminothiazoles EXCLI Journal 11 2012 453 467
9. C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine Journal of Molecular Graphics and Modelling 27 2008 188 196
10. C. Nantasenamat, T. Piacham, T. Tantimongcolwat, T. Naenna, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul QSAR model of the quorum-quenching N-acyl-homoserine lactone lactonase activity Journal of Biological Systems 16 2008 279 293
11. R. Pingaew, P. Tongraung, A. Worachartcheewan, C. Nantasenamat, S. Prachayasittikul, S. Ruchirawat, and V. Prachayasittikul Cytotoxicity and QSAR study of (thio)ureas derived from phenylalkylamines and pyridylalkylamines Medicinal Chemistry Research 22 2013 4016 4029
12. C. Thippakorn, T. Suksrichavalit, C. Nantasenamat, T. Tantimongcolwat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul Modeling the LPS neutralization activity of anti-endotoxins Molecules 14 2009 1869 1888
13. A. Worachartcheewan, C. Nantasenamat, C. Isarankura-Na-Ayudhya, S. Prachayasittikul, and V. Prachayasittikul Predicting the free radical scavenging activity of curcumin derivatives Chemometrics and Intelligent Laboratory Systems 109 2011 207 216
14. A. Worachartcheewan, C. Nantasenamat, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul Predicting antimicrobial activities of benzimidazole derivatives Medicinal Chemistry Research 22 2013 5418 5430
15. A. Worachartcheewan, C. Nantasenamat, T. Naenna, C. Isarankura-Na- Ayudhya, and V. Prachayasittikul Modeling the activity of furin inhibitors using artificial neural network European Journal of Medicinal Chemistry 44 2009 1664 1673
16. A. Worachartcheewan, C. Nantasenamat, W. Owasirikul, T. Monnor, O. Naruepantawart, S. Janyapaisarn, S. Prachayasittikul, and V. Prachayasittikul Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression European Journal of Medicinal Chemistry 73 2014 258 264
17. C. Nantasenamat, A. Worachartcheewan, P. Mandi, T. Monnor, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul QSAR modeling of aromatase inhibition by flavonoids using machine learning approaches Chemical Papers 68 2014 697 713
18. C. Nantasenamat, A. Worachartcheewan, S. Prachayasittikul, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul QSAR modeling of aromatase inhibitory activity of 1-substituted 1,2,3-triazole analogs of letrozole European Journal of Medicinal Chemistry 69 2013 99 114
19. A. Worachartcheewan, C. Nantasenamat, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum Chemical Papers 67 2013 1462 1473
20. C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul Quantitative structure-imprinting factor relationship of molecularly imprinted polymers Biosensors & Bioelectronics 22 2007 3309 3317
21. C. Nantasenamat, C. Isarankura-Na-Ayudhya, N. Tansila, T. Naenna, and V. Prachayasittikul Prediction of GFP spectral properties using artificial neural network Journal of Computational Chemistry 28 2007 1275 1289
22. C. Nantasenamat, T. Naenna, C. Isarankura Na-Ayudhya, and V. Prachayasittikul Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network Journal of Computer-Aided Molecular Design 19 2005 509 524
23. C. Nantasenamat, K. Srungboonmee, S. Jamsak, N. Tansila, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul Quantitative structure-property relationship study of spectral properties of green fluorescent protein with support vector machine Chemometrics and Intelligent Laboratory Systems 120 2013 42 52
24. M. Lapins, A. Worachartcheewan, O. Spjuth, V. Georgiev, V. Prachayasittikul, C. Nantasenamat, and J.E. Wikberg A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms PLoS One 8 2013 e66566
25. C. Nantasenamat, C. Isarankura-Na-Ayudhya, T. Naenna, and V. Prachayasittikul A practical overview of quantitative structure-activity relationship EXCLI Journal 8 2009 74 88
26. C. Nantasenamat, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul Advances in computational methods to predict the biological activity of compounds Expert Opinion on Drug Discovery 5 2010 633 654
27. W.S. Sun, Y.S. Park, J. Yoo, K.D. Park, S.H. Kim, J.H. Kim, and H.J. Park Rational design of an indolebutanoic acid derivative as a novel aldose reductase inhibitor based on docking and 3D QSAR studies of phenethylamine derivatives Journal of Medicinal Chemistry 46 2003 5619 5627
28. Y.S. Prabhakar, M.K. Gupta, N. Roy, and Y. Venkateswarlu A high dimensional QSAR study on the aldose reductase inhibitory activity of some flavones: topological descriptors in modeling the activity Journal of Chemical Information and Modeling 46 2006 86 92
29. S.V. Sambasivarao, L.K. Soni, A.K. Gupta, P. Hanumantharao, and S.G. Kaskhedikar Quantitative structure-activity analysis of 5-arylidene-2,4- thiazolidinediones as aldose reductase inhibitors Bioorganic & Medicinal Chemistry Letters 16 2006 512 520
30. L. Hu, G. Chen, and R.M. Chau A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors Journal of Molecular Graphics and Modelling 24 2006 244 253
31. J.C. Patra, and B.H. Chua Artificial neural network-based drug design for diabetes mellitus using flavonoids Journal of Computational Chemistry 32 2011 555 567
32. J.C. Patra, and O. Singh Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus Journal of Computational Chemistry 30 2009 2494 2508
33. S. Thareja, S. Aggarwal, T.R. Bhardwaj, and M. Kumar 3D-QSAR studies on a series of 5-arylidine-2, 4-thiazolidinediones as aldose reductase inhibitors: a self-organizing molecular field analysis approach Journal of Medicinal Chemistry 6 2010 30 36
34. L.K. Soni, A.K. Gupta, and S.G. Kaskhedikar Exploration of QSAR modelling techniques and their combination to rationalize the physicochemical characters of nitrophenyl derivatives towards aldose reductase inhibition Journal of Enzyme Inhibition and Medicinal Chemistry 24 2009 1002 1007
35. S.V. Jain, K.S. Bhadoriya, and S.B. Bari QSAR and flexible docking studies of some aldose reductase inhibitors obtained from natural origin Medicinal Chemistry Research 21 2012 1665 1676
36. B.L. Mylari, S.J. Armento, D.A. Beebe, E.L. Conn, J.B. Coutcher, M.S. Dina, M.T. O'Gorman, M.C. Linhares, W.H. Martin, P.J. Oates, D.A. Tess, G.J. Withbroe, and W.J. Zembrowski A novel series of non-carboxylic acid, non-hydantoin inhibitors of aldose reductase with potent oral activity in diabetic rat models: 6-(5-chloro-3-methylbenzofuran-2-sulfonyl)-2H-pyridazin-3- one and congeners Journal of Medicinal Chemistry 48 2005 6326 6339
37. B.L. Mylari, S.J. Armento, D.A. Beebe, E.L. Conn, J.B. Coutcher, M.S. Dina, M.T. O'Gorman, M.C. Linhares, W.H. Martin, P.J. Oates, D.A. Tess, G.J. Withbroe, and W.J. Zembrowski A highly selective, non-hydantoin, non-carboxylic acid inhibitor of aldose reductase with potent oral activity in diabetic rat models: 6-(5-chloro-3-methylbenzofuran- 2-sulfonyl)-2-H-pyridazin-3-one Journal of Medicinal Chemistry 46 2003 2283 2286
38. C. Nantasenamat, H. Li, P. Mandi, A. Worachartcheewan, T. Monnor, C. Isarankura-Na-Ayudhya, and V. Prachayasittikul Exploring the chemical space of aromatase inhibitors Molecular Diversity 17 2013 661 677
39. M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery, J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J.M. Millam, M. Klene, J.E. Knox, 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, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, and D.J. Fox Gaussian 09, Revision A.1 2009 Wallingford, Connecticut
40. Talete srl DRAGON for Windows (Software for Molecular Descriptor Calculations), Version 5.5 2007 Milano, Italy
41. I.H. Witten, E. Frank, and M.A. Hall Data Mining: Practical Machine Learning Tools and Techniques third ed. 2011 Morgan Kaufmann Amsterdam, Netherlands