Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices

Journal of Chemical Information and Modeling

Volumn 49, Issue 4, 2009, Pages 788-799

  Albaugh, Daniel R ^a   Hall, L Mark ^b   Hill, Dennis W ^a   Kertesz, Tzipporah M ^a   Parham, Marc ^c   Hall, Lowell H ^d   Grant, David F ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b Hall Associates Consulting   (United States)

^c Interactive Analysis   (United States)

^d EASTERN NAZARENE COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; ERROR STATISTICS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; LINEAR REGRESSION; MOLECULAR STRUCTURE;

BACK PROPAGATION ARTIFICIAL NEURAL NETWORK (BPANN); ENSEMBLE MODELING; EXPERIMENTAL VALUES; MEAN ABSOLUTE ERROR; MOLECULAR CONNECTIVITY; MULTIPLE LINEAR REGRESSIONS; NON-LINEAR RELATIONSHIPS; STRUCTURE INFORMATION;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CHEMICAL MODEL; CLUSTER ANALYSIS; DOCUMENTATION; FACTUAL DATABASE; FORECASTING; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REPRODUCIBILITY; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; CLUSTER ANALYSIS; DATABASES, FACTUAL; FORECASTING; LINEAR MODELS; MODELS, CHEMICAL; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REPRODUCIBILITY OF RESULTS; SUBJECT HEADINGS;

EID: 66149100383     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci9000162     Document Type: Article

References (39)

1. Li, X.; Luan, F.; Si, H.; Hu, Z.; Liu, M. Prediction of retention times for a large set of pesticides or toxicants based on support vector machine and the heuristic method. Toxicol. Lett. 2007, 175, 136-144.
2. Guo, W.; Lu, Y.; Zheng, X. M. The predicting study for Chromatographie retention index of saturated alcohols by MLR and ANN. Talanta 2000, 51, 479-488.
3. Jalali-Heravi, M.; Kyani, A. Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach. J. Chem. Inf. Comput. Sci. 2004, 44, 1328-1335.
4. Acevedo-Martínez, J.; Escalona-Arranz, J. C.; Villar-Rojas, A.; Téllez-Palmero, F.; Pérez-Rosés, R.; González, L.; Carrasco-Velar, R. Quantitative study of the structure-retention index relationship in the imine family. J. Chromatogr., A. 2006, 1102, 238-244.
5. Hadjmohammadi, M. R.; Fatemi, M. H.; Kamel, K. Quantitative structure-property relationship study of retention time of some pesticides in gas chromatography. J. Chromatogr. Sci. 2007, 45, 400-404.
6. Rouhollahi, A.; Shafieyan, H.; Ghasemi, J. B. A QSPR study on the GC retention times of a series of fatty, dicarboxylic and amino acids by MLR and ANN. Ann. Chim. 2007, 97, 925-933.
7. Konoz, E.; Fatemi, M. H.; Faraji, R. Prediction of Ko vats retention indices of some aliphatic aldehydes and ketones on some stationary phases at different temperatures using artificial neural network. J. Chromatogr. Sci. 2008, 46, 406-412.
8. Quiming, N. S.; Denola, N. L.; Saito, Y.; Jinno, K. Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography. J. Sep. Sci. 2008, 31, 1550-1563.
9. Hill, D. W.; Kind, A. J. Reversed-phase solvent-gradient HPLC retention indexes of drugs. J. Anal. Toxicol. 1994, 18, 233-242.
10. Hall, L. H. A structure-information approach to the prediction of biological activities and properties. Chem. Biodiversity 2004, 1, 183-201.
11. Hall, L. H.; Hall, L. M. QSAR modeling based on structure-information for properties of interest in human health. SAR OSAR Environ. Res. 2005, 16, 13-41.
12. Kier, L. B.; Hall, L. H. Molecular Structure Description, the Electrotopological State; Academic Press: San Diego, 1999; pp 13-35.
13. Kier, L. B.; Hall, L. H. Molecular Connectivity in Chemistry and Drug Research; Academic Press: New York, 1976.
14. Kier, L. B.; Hall, L. H. Molecular Connectivity in Structure-Activity Analysis; John Wiley: New York, 1986.
15. Hu, Q. N.; Liang, Y. Z.; Yin, H.; Peng, X. L.; Fang, K. T. Structural interpretation of the topological index. 2. The molecular connectivity index, the Kappa index, and the atom-type E-State index. J. Chem. Inf. Comput. Sci. 2004, 44, 1193-1201.
16. Wackermann, J.; Allefeld, C. On the meaning and interpretation of global descriptors of brain electrical activity. Including a reply to X. Pei et al. Int. J. Psvchovhysiol. 2007, 64, 199-210.
17. Putta, S.; Lemmen, C.; Beroza, P.; Greene, J. A novel shape-feature based approach to virtual library screening. J. Chem. Inf. Comput. Sci. 2002, 42, 1230-1240.
18. Abolmaali, S. F.; Wegner, J. K.; Zell, A. The compressed feature matrix-a fast method for feature based substructure search. J. Mol. Model. 2003, 9, 235-241.
19. Maw, H. H.; Hall, L. H. E-state modeling of dopamine transporter binding. Validation of the model for a small data set. J. Chem. Inf. Comput. Sci. 2000, 40, 1270-1275.
20. Maw, H. H.; Hall, L. H. E-state modeling of corticosteroids binding affinity validation of model for small data set. J. Chem. Inf. Comput. Sci. 2001, 41, 1248-1254.
21. Votano, J. R.; Parham, M.; Hall, L. H.; Kier, L. B.; Oloff, S.; Tropsha, A.; Xie, Q.; Tong, W. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis 2004, 19, 365-377.
22. Votano, J. R.; Parham, M.; Hall, L. M.; Hall, L. H.; Kier, L. B.; Oloff, S.; Tropsha, A. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem. 2006, 49, 7169-7181.
23. The Pubchem Project, http://pubchem.ncbi.nlm.nih.gov/ (accessed Feb 17, 2009).
24. winMolconn; Hall Associates Consulting: Quincy, MA, 2008.
25. Huuskonen, J. Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. J. Chem. Inf. Comput. Sci. 2000, 40, 773-777
26. Patankar, S. J.; Jurs, P. C. Prediction of IC50 values for ACAT inhibitors from molecular structure. J. Chem. Inf. Comput. Sci. 2000, 40, 706-723.
27. Tetko, I. V.; Tanchuk, V. Y.; Villa, A. E. Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices. J. Chem. Inf. Comput. Sci. 2001, 41, 1407-1421.
28. Roy, K.; Chakraborty, S.; Saha, A. Exploring selectivity requirements for COX-2 versus COX-1 binding of 3,4-diaryloxazolones using E-state index. Bioorg. Med. Chem. Lett. 2003, 13, 3753-3757.
29. Votano, J. R.; Parham, M.; Hall, L. H.; Kier, L. B.; Oloff, S.; Tropsha, A.; Xie, Q.; Tong, W. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis 2004, 19, 365-377.
30. Wegner, J. K.; Fröhlich, H.; Zeil, A. Feature selection for descriptor based classification models. 2. Human intestinal absorption (HIA). J. Chem. Inf. Comput. Sci. 2004, 44, 931-939.
31. Hall, L. H.; Kier, L. B. The Relation of Molecular Connectivity to Molecular Volume and Biological Activity. Eur. J. Med. Chem. 1981, 16, 399-407.
32. Hall, L. H.; Kier, L. B. The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Relations. In Reviews of Computational Chemistry; 1991; pp 367-422.
33. MDL QSAR version 2.2 b365; SymyxTechnology MDL: San Ramon, CA, 2003.
34. JMP, version 4.04; SAS Institute Inc.: Cary, NC.
35. Aisa, B.; Mingus, B.; O'Reilly, R. The Emergent Neural Modeling System. Neural Networks 2008, 21, 1045-1212.
36. Daniel, D.; Wood, F. S. Fitting Equations to Data; Wiley-Interscience, John Wiley and Sons: New York, 1980.
37. Hill, D. W.; Kertesz, T. M.; Fontaine, D.; Friedman, R.; Grant, D. F. Mass Spectral Metabonomics beyond Elemental Formula: Chemical Database Querying by Matching Experimental with Computational Fragmentation Spectra. Anal. Chem. 2008, 80, 5574-5582.
38. Geman, S.; Bienenstock, E.; Doursat, R. Neural networks and the bias/variance dilemma. Neural Computation 1992, 4, 1-58.
39. Jaworska, J. QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review. ATLA 2005, 3, 445-459.