Structure-flammability relationship study of phosphoester dimers by MLR and PLSa
Crisan, Luminita; Iliescu, Smaranda; Funar-Timofei, Simona
http://dx.doi.org/10.1590/0104-1428.2306
Polímeros: Ciência e Tecnologia, vol.26, n2, p.129-136, 2016
Abstract
Polyphosphonates and polyphosphates having good flame retardancy represent an important class of organophosphorus based polymer additives. In this analysis the flammability of 28 previously synthesized polyphosphoesters, modelled as dimmers, was explored using the multiple linear regression (MLR) and Partial Least Square (PLS) methodology. The statistical quality of the final MLR and PLS models was estimated using the following parameters: the squared correlation coefficient ( = 0.917 and 0.976), the training root-mean-square errors (RMSEtr = 0.029 and 0.016) and the leave-seven-out cross-validation correlation coefficient ( = 0.748 and 0.881), respectively. External validation was checked for a test set of seven compounds using several criteria. The MLR models had somewhat inferior fitting results. The final MLR and PLS models can be used for the estimation of limiting oxygen index (LOI) values of new polyphosphoester structures. The presence of phosphonate groups and increasing molecular branching in an isomeric series favour the dimer flammability.
Keywords
quantitative structure-property relationships, polyphosphonate, polyphosphate, limiting oxygen index, flame retardancy.
References
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2. Le, T., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012). Quantitative structure–property relationship modeling of diverse materials properties. Chemical Reviews, 112(5), 2889-2919. http://dx.doi.org/10.1021/cr200066h. PMid:22251444.
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4. Troev, K. D. (2012). Polyphosphoesters: chemistry and application (pp. 263-320). London: Elsevier Insights.
5. Chen, L., & Wang, Y. Z. (2010). Aryl polyphosphonates: useful halogen-free flame retardants for polymers. Materials, 3(10), 4746-4760. http://dx.doi.org/10.3390/ma3104746.
6. Funar-Timofei, S., Iliescu, S., & Suzuki, T. (2014). Correlations of limiting oxygen index with structural polyphosphoester features by QSPR approaches. Structural Chemistry, 25(6), 1847-1863. http://dx.doi.org/10.1007/s11224-014-0474-7.
7. Iliescu, S., Avram, E., Visa, A., Plesu, N., Popa, A., & Ilia, G. (2011). New technique for the synthesis of polyphosphoesters. Macromolecular Research, 19(11), 1186-1191. http://dx.doi.org/10.1007/s13233-011-1111-6.
8. ChemAxon. (2015). Marvin sketch 15.2.16 software. Záhony: ChemAxon. Retrieved in 27 April 2015, from http://www.chemaxon.com
9. Halgren, T. A. (1999). MMFF VI.MMFF94s option for energy minimization studies. Journal of Computational Chemistry, 20(7), 720-729. http://dx.doi.org/10.1002/(SICI)1096-987X(199905)20:7<720::AID-JCC7>3.0.CO;2-X.
10. OpenEye Scientific. (2013). OMEGA version 2.5.1.4 software. Santa Fe: OpenEye Scientific. Retrieved in 29 April 2015, from http://www.eyesopen.com
11. Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A., & Stahl, M. T. (2010). Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. Journal of Chemical Information and Modeling, 50(4), 572-584. http://dx.doi.org/10.1021/ci100031x. PMid:20235588.
12. Hawkins, P. C. D., & Nicholls, A. (2012). Conformer generation with OMEGA: learning from the data set and the analysis of failures. Journal of Chemical Information and Modeling, 52(11), 2919-2936. http://dx.doi.org/10.1021/ci300314k. PMid:23082786.
13. Talete SRL. (2007). Dragon professional 5.5 software. Milano: Talete SRL. Retrieved in 4 May 2015, from http://www.talete.mi.it
14. ChemAxon. (2015). Instant JChem 15.2.23 software. Záhony: ChemAxon. Retrieved in 4 May 2015, from http://www.chemaxon.com
15. Todeschini, R., Consonni, V., Mannhold, R., Kubinyi, H., & Folkers, G. (Eds.). (2009). Molecular descriptors for chemoinformatics. Weinheim: Wiley – VCH.
16. Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: an introduction to cluster analysis. New York: Wiley.
17. R Development Core Team. (2011). R: A language and environment for statistical computing. Version 2.13.1. Vienna: R Foundation for Statistical Computing. Retrieved in 11 May 2015, from www.r-project.org
18. Wold, S., &Dunn, W. J.3rd (1983). Multivariate quantitative structure-activity relationships (QSAR):conditions for their applicability. Journal of Chemical Information and Computer Sciences, 23(1), 6-13. http://dx.doi.org/10.1021/ci00037a002.
19. Chirico, N., Papa, E., Kovarich, S., Cassani, S., & Gramatica, P. (2012). QSARINS, software for QSAR MLR model development and validation. Varese: University of Insubria/QSAR Res Unit in Environ Chem and Ecotox., DiSTA. Retrieved in 11 May 2015, from http://www.qsar.it
20. Gramatica, P., Chirico, N., Papa, E., Cassani, S., & Kovarich, S. (2013). A new software for the development, analysis, and validation of QSAR MLR models. Journal of Computational Chemistry, 34(24), 2121-2132. http://dx.doi.org/10.1002/jcc.23361.
21. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130. http://dx.doi.org/10.1016/S0169-7439(01)00155-1.
22. Shi, L. M., Fang, H., Tong, W., Wu, J., Perkins, R., Blair, R. M., Branham, W. S., Dial, S. L., Moland, C. L., & Sheehan, D. M. (2001). QSAR models using a large diverse set of estrogens. Journal of Chemical Information and Modeling, 41(1), 186-195. http://dx.doi.org/10.1021/ci000066d. PMid:11206373.
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24. Consonni, V., Ballabio, D., & Todeschini, R. (2009). Comments on the definition of the Q2 parameter for QSAR validation. Journal of Chemical Information and Modeling, 49(7), 1669-1678. http://dx.doi.org/10.1021/ci900115y. PMid:19527034.
25. Chirico, N., & Gramatica, P. (2011). Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. Journal of Chemical Information and Modeling, 51(9), 2320-2335. http://dx.doi.org/10.1021/ci200211n. PMid:21800825.
26. Goodarzi, M., Deshpande, S., Murugesan, V., Katti, S. B., & Prabhakar, Y. S. (2009). Is feature selection essential for ANN modeling? QSAR & Combinatorial Science, 28(11-12), 1487-1499. http://dx.doi.org/10.1002/qsar.200960074.
27. Roy, P. P., Paul, S., Mitra, I., &Roy, K. (2009). On two novel parameters for validation of predictive QSAR models. Molecules, 14(5), 1660-1701. http://dx.doi.org/10.3390/molecules14051660. PMid:19471190.
28. Chirico, N., & Gramatica, P. (2012). Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. Journal of Chemical Information and Modeling, 52(8), 2044-2058. http://dx.doi.org/10.1021/ci300084j. PMid:22721530.
29. Tropsha, A., & Golbraikh, A. (2010). Predictive quantitative structure–activity relationships modeling: development and validation of QSAR models. In J. L. Faulon & A. Bender (Ed). Handbook of chemoinformatics algorithms (pp. 213-233). London: Chapman & Hall/CRC.
30. Gramatica, P. (2007). Principles of QSAR models validation: internal and external. QSAR & Combinatorial Science, 26(5), 694-701. http://dx.doi.org/10.1002/qsar.200610151.
31. Balaban, A. T. (1983). Topological indices based on topological distances in molecular graphs. Pure and Applied Chemistry, 5(2), 199-206. http://dx.doi.org/10.1351/pac198855020199.
32. Umetrics AB. (2013). SIMCA-P+ version 12.0 software. Umea: Umetrics. Retrieved in 18 May 2015, from http://www.umetrics.com
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