Three-class classification models of logS and logP derived by using GA-CG-SVM approach

Molecular Diversity

Volumn 13, Issue 2, 2009, Pages 261-268

  Zhang, Hui ^a,b   Xiang, Ming Li ^a,b   Ma, Chang Ying ^a   Huang, Qi ^a   Li, Wei ^a   Xie, Yang ^a   Wei, Yu Quan ^a   Yang, Sheng Yong ^a  

^a SICHUAN UNIVERSITY   (China)

^b SICHUAN UNIVERSITY   (China)

Author keywords

Aqueous solubility; Feature selection; Lipophilicity; Parameter optimization; Support vector machine (SVM)

Indexed keywords

ACCURACY; AQUEOUS SOLUTION; ARTICLE; CLASSIFICATION; CONJUGATE; GENETIC ALGORITHM; LIPOPHILICITY; PARAMETER; PRIORITY JOURNAL; SOLUBILITY; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GENETICS; HYDROPHOBICITY; MODELS, CHEMICAL; SENSITIVITY AND SPECIFICITY; SOLUBILITY; WATER;

EID: 67349205580     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-009-9108-1     Document Type: Article

References (40)

1. Hvd Waterbeemd E Gifford 2003 ADMET in silico modelling: towards prediction paradise Nat Rev Drug Discov 2 192 204 10.1038/nrd1032
2. Poda G, Tetko L, Rohrer DC (2005) Towards predictive ADME profiling of drug candidates: lipophilicity and solubility. 229th ACS National Meeting, San Diego, CA, USA, March 13-17
3. H Sun 2004 A universal molecular descriptor system for prediction of logP, logS, logBBB, and absorption J Chem Inf Comput Sci 44 748 757 10.1021/ci030304f
4. Q Liao J Yao S Yuan 2006 SVM approach for predicting logP Mol Divers 10 301 309
5. M Lobell V Sivarajah 2003 In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values Mol Divers 7 69 87
6. XQ Chen SJ Cho Y Li 2002 Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship J Pharm Sci 91 1838 1852
7. CAS Bergström CM Wassvik U Norinder 2004 Global and local computational models for prediction of aqueous solubility of drug-like molecules J Chem Inf Comput Sci 44 1477 1488
8. O Engkvist P Wrede 2002 In silico prediction of aqueous solubility based on one- and two-dimensional descriptors J Chem Inf Comput Sci 42 1247 1249
9. CAS Bergström 2005 Computational models to predict aqueous drug solubility, permeability and intestinal absorption Expert Opin Drug Metab Toxicol 1 613 627
10. TJ Hou K Xia W Zhang 2004 ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach J Chem Inf Comput Sci 44 266 275
11. R Mannhold Hvd Waterbeemd 2001 Substructure and whole molecule approaches for calculating logP J Comput Aid Mol Des 15 337 354
12. J Taskinen J Yliruusi 2003 Prediction of physicochemical properties based on neural network modeling Adv Drug Deliv Rev 55 1163 1183
13. JR Votano M Parham LH Hall 2004 Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation Chem Biodivers 1 1829 1841
14. B Fredsted PB Brockhoff C Vind 2007 In silico classification of solubility using Binary k-Nearest Neighbor and physicochemical descriptors QSAR Comb Sci 26 452 459
15. DT Manallack BG Tehan E Gancia 2003 A consensus neural network-based technique for discriminating soluble and poorly soluble compounds J Chem Inf Comput Sci 43 674 679
16. FL Stahura JW Godden J Bajorath 2002 Differential shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations J Chem Inf Comput Sci 42 550 558 10.1021/ci010243q
17. Vapnik V (1998) Statistical learning theory. Wiley, New York
18. CJC Burges 1998 A tutorial on support vector machines for pattern recognition Data Min Knowl Disc 2 127 167
19. MWB Trotter SB Holden 2003 Support vector machines for ADME property classification QSAR Comb Sci 22 533 548 10.1002/qsar.200310006
20. H Li CW Yap Y Xue 2006 Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents Drug Dev Res 66 245 259 10.1002/ddr.20044
21. Y Xue CW Yap LZ Sun 2004 Prediction of P-glycoprotein substrates by a support vector machine approach J Chem Inf Comput Sci 44 1497 1505
22. H Li CW Yap CY Ung 2005 Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods J Chem Inf Model 45 1376 1384 10.1021/ci050135u
23. CL Huang CJ Wang 2006 A GA-based feature selection and parameters optimization for support vector machines Expert Syst Appl 31 231 240 10.1016/j.eswa.2005.09.024
24. Feature selection for supportvector machines by means of genetic algorithms. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, CA, USA, pp 142-148
25. H Fröhlich JK Wegner A Zell 2004 Towards optimal descriptor subset selection with support vector machines in classification and regression QSAR Comb Sci 23 311 318
26. SY Yang Q Huang LL Li 2008 An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs Artif Intell Med 10.1016/j.artmed.2008.07.001
27. CY Ma SY Yang H Zhang 2008 Prediction models of human plasma protein binding rate and oral bioavailability derived y using GA-CG-SVM method J Pharm Biomed Anal 47 677 682
28. H Zhang QY Chen ML Xiang 2008 In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach Toxicol In Vitro 10.1016/j.tiv.2008.09.017
29. Davis, L (eds) (1991) Handbook of genetic algorithms Handbook of genetic algorithms. Van Nostrand Reinhold, New York
30. Keerthi S, Sindhwani V, Chapelle O (2007) An efficient method for gradient-based adaptation of hyperparameters in SVM models. In: Schølkopf B, Platt J, Hofmann T (eds) Advances in neural information processing systems~20 (NIPS~2006). Vancouver, Canada
31. CW Hsu CJ Lin 2002 A comparison of methods for multiclass support vector machines IEEE Trans Neural Netw 13 415 425 10.1109/TNN.2002.1000139
32. SR Johnson W Zheng 2006 Recent progress in the computational prediction of aqueous solubility and absorption AAPS J 8 E27 E40 10.1208/aapsj080104
33. AX Yan J Gasteiger 2003 Prediction of aqueous solubility of organic compounds by topological descriptors QSAR Comb Sci 22 821 829 10.1002/qsar.200330822
34. LogP data. Available via: http://ambit.acad.bg/ambit/data/kowwin- validation.csv
35. Weblabviewer, Accelrys Inc. San Diego, USA. Cerius2, verion4.10 (2005) Available via: http://www.accelrys.com
36. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Available via: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/ guide.pdf
37. Chang CC, Lin CJ (2001) LIBSVM: A library for support vector machines. Available via: http://www.csie.ntu.edu.tw/~cjlin/libsvm
38. VCCLAB (2005) Virtual Computational Chemistry Laboratory. Available via: http://www.vcclab.org
39. S Degroeve B De Baets Y Vande Peer 2002 Feature subset selection for splice site prediction Bioinformatics 18 S75 S83
40. P Baldi S Brunak Y Chauvin 2000 Assessing the accuracy of prediction algorithms for classification: an overview Bioinformatics 16 412 424 10.1093/bioinformatics/16.5.412