High-dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues based on support vector regression (SVR)

International Journal of Molecular Sciences

Volumn 13, Issue 1, 2012, Pages 1161-1172

  Zhou, Wei ^a,b   Dai, Zhijun ^a   Chen, Yuan ^a   Wang, Haiyan ^c   Yuan, Zheming ^a,b  

^a Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization   (China)

^b HUNAN AGRICULTURAL UNIVERSITY   (China)

^c KANSAS STATE UNIVERSITY   (United States)

Author keywords

ARC 111 analogues; High dimensional descriptor selection nonlinearly (HDSN) method; QSAR; RPMI8402; Support vector regression; Worst descriptor elimination multi roundly (WDEM) method

Indexed keywords

8,9 DIMETHOXY 5 (2 N,N DIMETHYLAMINOEETHYL) 2,3 METHYLENEDIOXY 5H DIBENZO[C,H][1,6] NAPTHYRIDIN 6 ONE; ANTINEOPLASTIC AGENT; ARC 111; UNCLASSIFIED DRUG; NAPHTHYRIDINE DERIVATIVE; TOPOVALE;

ANTINEOPLASTIC ACTIVITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; DRUG DESIGN; DRUG SCREENING; DRUG STRUCTURE; HIGH DIMENSIONAL DESCRIPTOR SELECTION NONLINEARLY METHOD; INTERMETHOD COMPARISON; MACHINE LEARNING; NONBIOLOGICAL MODEL; PREDICTIVE VALUE; PROCESS OPTIMIZATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; TUMOR CELL; WORST DESCRIPTOR ELIMINATION MULTI ROUNDLY METHOD; CHEMISTRY;

ANTINEOPLASTIC AGENTS; NAPHTHYRIDINES; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINES;

EID: 84863021520     PISSN: None     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms13011161     Document Type: Article

References (32)

1. Rasheed, Z.A.; Rubin E.H. Mechanisms of resistance to topoisomerase I-targeting drugs. Oncogene 2003, 22, 7296-7304.
2. Li, T.K.; Houghton, P.J.; Desai, S.D.; Daroui, P.; Liu, A.A.; Hars, E.S.; Ruchelman, A.L.; LaVoie, E.J.; Liu L.F. Characterization of ARC-111 as a novel Topoisomerase I-targeting anticancer drug. Cancer Res. 2003, 63, 8400-8407.
3. Ruchelman, A.L.; Houghton, P.J.; Zhou, N.; Liu, A.; Liu, L.F.; LaVoie, E.J. 5-(2-Aminoethyl)dibenzo[c,h][1,6]naphthyridin-6-ones: variation of N-alkyl substituents modulates sensitivity to efflux transporters associated with multidrug resistance. J. Med. Chem. 2005, 48, 792-804.
4. Yu, Y.J.; Su, R.X.; Wang, L.B.; Qi, W.; He, Z.M. Comparative QSAR modeling of antitumor activity of ARC-111 analogues using stepwise MLR, PLS, and ANN techniques. Med. Chem. Res. 2010, 19, 1233-1244.
5. Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; et al. Virtual computational chemistry laboratory - design and description. J. Comput. Aid. Mol. Des. 2005, 19, 453-463.
6. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods; Cambridge University Press: Cambridge, UK, 2000.
7. Tan, X.S.; Yuan, Z.M.; Zhou, T.J.; Wang, C.J.; Xiong, J.Y. Multi-KNN-SVR combinatorial forecast and its application to QSAR of fluorine-containing compounds. Chem. J. Chin. Univ. 2008, 29, 95-99.
8. Dai, Z.J.; Zhou, W.; Yuan, Z.M. A novel method of nonlinear rapid feature selection for high-dimensional features and its application in peptide QSAR modeling based on support vector machine. Acta Phys. Chim. Sin. 2011, 27, 1654-1660.
9. Wang, L.F.; Tan, X.S.; Bai, L.Y.; Yuan, Z.M. Establishing an interpretability system for support vector regression and its application in QSAR of organophosphorus insecticide. Asian J. Chem. 2011, 24, in press.
10. Geerts, T.; Vander Heyden, Y. In-silico predictions of ADME-Tox properties: Drug absorption. Comb. Chem. High Throughput Screen. 2011, 14, 339-361.
11. Marković, V.; Erić, S.; Juranić, Z.D.; Stanojković, T.; Joksović, L.; Ranković, B.; Kosanić, M.; Joksović, M.D. Synthesis, antitumor activity and QSAR studies of some 4-aminomethylidene derivatives of edaravone. Bioorg. Chem. 2011, 39, 18-27.
12. Prabhakar, Y.S.; Rawal, R.K.; Gupta, M.K.; Solomon, V.R.; Katti, S.B. Topological descriptors in modeling the HIV inhibitory activity of 2-Aryl-3-pyridyl-thiazolidin-4-ones. Comb. Chem. High Throughput Screen. 2005, 8, 431-437.
13. Riahi, S.; Ganjali, M.R.; Pourbasheer, E.; Norouzi, P. QSRR study of GC retention indices of essential-oil compounds by multiple linear regression with a genetic algorithm. Chromatographia 2008, 67, 917-922.
14. Goudarzia, N.; Fatemib, M.H.; Samadi-Maybodib, A. Quantitative structure-properties relationship study of the 29Si-NMR chemical shifts of some silicate species. Spectrosc. Lett. 2009, 42, 186-193.
15. Cao, D.S.; Xu, Q.S.; Liang, Y.Z.; Chen, X.; Li, H.D. Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine. J. Chemometr. 2010, 24, 584-595.
16. Gozalbes, R.; Pineda-Lucena, A. QSAR-based solubility model for drug-like compounds. Bioorgan. Med. Chem. 2010, 18, 7078-7084.
17. Lapinsh, M.; Veiksina, S.; Uhlén, S.; Petrovska, R.; Mutule, I.; Mutulis, F.; Yahorava, S.; Prusis, P.; Wikberg, J.E.S. Proteochemometric mapping of the interaction of organic compounds with melanocortin receptor subtypes. Mol. Pharmacol. 2005, 67, 50-59.
18. Liang, G.Z.; Yang, S.B.; Zhou, Y.; Zhou, P.; Li, Z.L. Using scores of amino acid topological descriptors for quantitative sequence-mobility modeling of peptides based on support vector machine. Chin. Sci. Bull. 2006, 51, 2700-2705.
19. Bansal, R.; Karthikeyan, C.; Moorthy, N.S.H.N.; Trivedi, P. QSAR analysis of some phthalimide analogues based inhibitors of HIV-1 integrase. Arkivoc 2007, XV, 66-81.
20. Malik, A.; Singh, H.; Andrabi, M.; Husain, S.A.; Ahmad, S. Databases and QSAR for cancer research. Cancer Inform. 2006, 2, 99-111.
21. Lapins, M.; Wikberg, J.E. Proteochemometric modeling of drug resistance over the mutational space for multiple HIV protease variants and multiple protease inhibitors. J. Chem. Inf. Model. 2009, 49, 1202-1210.
22. Tanabe, K.; Lučić, B.; Amić, D.; Kurita, T.; Kaihara, M.; Onodera, N.; Suzuki, T. Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling. Mol. Divers. 2010, 14, 789-802.
23. Gharagheizi, F. An accurate model for prediction of autoignition temperature of pure compounds. J. Hazard. Mater. 2011, 189, 211-221.
24. Fedorowicz, A.; Zheng, L.; Singh, H.; Demchuk, E. QSAR study of skin sensitization using local lymph node assay data. Int. J. Mol. Sci. 2004, 5, 56-66.
25. González, M.P.; Terán, C.; Teijeira, M.; Besada, P.; González-Moa, M.J. BCUT descriptors for predicting affinity toward A3 adenosine receptors. Bioorg. Med. Chem. Lett. 2005, 15, 3491-3495.
26. Pasha, F.A.; Srivastava, H.K.; Srivastava, A.; Singh, P.P. QSTR study of small organic molecules against Tetrahymena pyriformis. QSAR Comb. Sci. 2007, 26, 69-84.
27. Putz, M.V.; Putz, A.-M.; Lazea, M.; Ienciu, L.; Chiriac, A. Quantum-SAR extension of the spectral-SAR algorithm. Application to polyphenolic anticancer bioactivity. Int. J. Mol. Sci. 2009, 10, 1193-1214.
28. Putz, M.V.; Lacrǎmǎ, A.-M. Introducing spectral structure activity relationship (S-SAR) analysis. Application to ecotoxicology. Int. J. Mol. Sci. 2007, 8, 363-391.
29. Lacrǎmǎ, A.-M.; Putz, M.V.; Ostafe, V. A spectral-SAR model for the anionic-cationic interaction in ionic liquids: application to vibrio fischeri ecotoxicity. Int. J. Mol. Sci. 2007, 8, 842-863.
30. Chicu, S.A.; Putz, M.V. Köln-Timişoara molecular activity combined models toward interspecies toxicity assessment. Int. J. Mol. Sci. 2009, 10, 4474-4497.
31. Chen, Y.; Yuan, Z.M.; Zhou, W.; Xiong, X.Y. A novel QSAR model based on geostatistics and support vector regression. Acta Phys. Chim. Sin. 2009, 25, 1587-1592.
32. Zhang, S.X.; Wei, L.Y.; Bastow, K.; Zheng, W.F.; Brossi, A.; Lee, K.H.; Tropsha, A. Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J. Comput. Aid. Mol. Des. 2007, 21, 97-112.