QSPR studies on refractive indices of structurally heterogeneous polymers
We developed a predictive Quantitative Structure-Property Relationship (QSPR) for the refractive indices of 234 structurally diverse polymers. The model involves a single molecular descriptor and a conformation-independent approach. The most appropriate polymer structure representation was investiga...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | , , , , |
| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2015
|
| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| Sumario: | We developed a predictive Quantitative Structure-Property Relationship (QSPR) for the refractive indices of 234 structurally diverse polymers. The model involves a single molecular descriptor and a conformation-independent approach. The most appropriate polymer structure representation was investigated by considering 1-5 monomeric repeating units. The established equations were validated and tested through various well-known techniques, such as the use of an external test set of compounds, the Cross-Validation method, Y-Randomization and Applicability Domain, and finally a comparison was also performed to published results from the li terature. The developed QSPR could be useful for assisting the development of new polymeric materials. © 2014 Elsevier B.V. |
|---|---|
| Bibliografía: | Knoll, W., Interfaces and thin films as seen by bound electromagnetic waves (1998) Ann. Rev. Phys. Chem., 49, pp. 569-638 Samuels, R.J., Application of refractive index measurements to polymer analysis (1981) J. Appl. Polym. Sci., 26, pp. 1383-1412 Hu, R., Lam, J.W.Y., Tang, B.Z., Recent progress in the development of new acetylenic polymers (2013) Macromol. Chem. Phys., 214, pp. 175-187 Schraub, M., Soll, S., Hampp, N., High refractive index coumarin-based photorefractive polysiloxanes (2013) Eur. Polym. J., 49, pp. 1714-1721 Sydlik, S.A., Chen, Z., Swager, T.M., Triptycene polyimides: soluble polymers with high thermal stability and low refractive indices (2011) Macromolecules, 44, pp. 976-980 van Krevelen, D.W., te Nijenhuis, K., (2009) Properties of Polymers: Their Correlation with Chemical Structure; Their Numerical Estimation and Prediction from Additive Group Contributions, , Elsevier, New York Hansch, C., Leo, A., Exploring QSAR (1995) Fundamentals and Applications in Chemistry and Biology, , American Chemical Society, Washington, D. C Kubinyi, H., (2008) QSAR: Hansch Analysis and Related Approaches, , Wiley-Interscience, New York (2010) Recent Advances in QSAR Studies: Methods and Applications, , Springer Science&Business Media B.V., Netherlands, T. Puzyn, J. Leszczynski, M.T.D. Cronin (Eds.) Katritzky, A.R., Goordeva, E.V., Traditional topological indexes vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research (1993) J. Chem. Inf. Comput. Sci., 33, pp. 835-857 Diudea, M.V.E., (2001) QSPR/QSAR Studies by Molecular Descriptors, , Nova Science Publishers, New York Todeschini, R., Consonni, V., (2009) Molecular Descriptors for Chemoinformatics (Methods and Principles in Medicinal Chemistry), , Wiley-VCH, Weinheim Golbraikh, A., Tropsha, A., Beware of q 2! (2002) J. Mol. Graphics Modell., 20, pp. 269-276 Hawkins, D.M., Basak, S.C., Mills, D.J., Assessing model fit by cross-validation (2003) Chem. Inf. Model., 43, pp. 579-586 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 (2009) Int. J. Mol. Sci., 10, pp. 1193-1214 Putz, M.V., Ionascu, C., Putz, A.-M., Ostafe, V., Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis (2011) Int. J. Mol. Sci., 12, pp. 5098-5134 Bicerano, J., (1996) Prediction of Polymer Properties, , Marcel Dekker Inc., New York García-Domenech, R., de Julián-Ortiz, J.V., Prediction of indices of refraction and glass transition temperatures of linear polymers by using graph theoretical indices (2002) J. Phys. Chem. B, 106, pp. 1501-1507 Hawkins, D.M., The problem of overfitting (2004) J. Chem. Inf. Comput. Sci., 44, pp. 1-12 Katritzky, A.A., Sild, S., Karelson, M., Correlation and prediction of the refractive indices of polymers by QSPR (1998) J. Chem. Inf. Comput. Sci., 38, pp. 1171-1176 Xu, J., Chen, B., Zhang, Q.J., Guo, B., Prediction of refractive indices of linear polymers by a four-descriptor QSPR model (2004) Polymer, 45, pp. 8651-8659 Astray, G., Cid, A., Moldes, O., Ferreiro-Lage, J.A., Gálvez, J.F., Mejuto, J.C., Prediction of refractive index of polymers using artificial neural networks (2010) J. Chem. Eng. Data, 55, pp. 5388-5393 Duchowicz, P.R., Comelli, N.C., Ortiz, E.V., Castro, E.A., QSAR study for carcinogenicity in a large set of organic compounds (2012) Curr. Drug Saf., 7, pp. 282-288 Talevi, A., Bellera, C.L., Ianni, M.D., Duchowicz, P.R., Bruno-Blanch, L.E., Castro, E.A., An integrated drug development approach applying topological descriptors (2012) Curr. Comput. Aided Drug Des., 8, pp. 172-181 Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., OCWLGI descriptors: theory and praxis (2013) Curr. Comput. Aided Drug Des., 9, pp. 226-232 Mullen, L.M.A., Duchowicz, P.R., Castro, E.A., QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents (2011) Chemom. Intell. Lab. Syst., 107, pp. 269-275 García, J., Duchowicz, P.R., Rozas, M.F., Caram, J.A., Mirífico, M.V., Fernández, F.M., Castro, E.A., A comparative QSAR on 1, 2, 5-thiadiazolidin-3-one 1, 1-dioxide compounds as selective inhibitors of human serine proteinases (2011) J. Mol. Graphics Modell., 31, pp. 10-19 Ibezim, E., Duchowicz, P.R., Ortiz, E.V., Castro, E.A., QSAR on aryl-piperazine derivatives with activity on malaria (2012) Chemom. Intell. Lab. Syst., 110, pp. 81-88 Toropov, A.A., Toropova, A.P., Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants (2001) J. Mol. Struct. Theochem, 538, pp. 287-293 Toropov, A.A., Nesterov, I.V., Nabiev, O.M., QSAR modeling of dihydrofolate reductase inhibitory activity by correlation weighting of nearest neighboring codes (2003) J. Mol. Struct. Theochem, 622, pp. 269-273 Toropov, A.A., Leszczynska, D., Leszczynski, J., Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector (2007) J. Comput. Biol. Chem., 31, pp. 127-128 Toropov, A.A., Leszczynska, D., Leszczynski, J., Predicting thermal conductivity of nanomaterials by correlation weighting technological attributes codes (2007) Mater. Lett., 61, pp. 4777-4780 Toropov, A.A., Benfenati, E., Additive, E., SMILES-based optimal descriptors in QSAR modelling bee toxicity: using rare SMILES attributes to define the applicability domain (2008) Bioorg. Med. Chem., 26, pp. 4801-4809 Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Puzyn, T., Leszczynska, D., Leszczynski, J., Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (2012) Chemosphere, 89, pp. 1098-1102 Toropov, A.A., Toropova, A.P., Martyanov, S.E., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines (2011) Chemom. Intell. Lab. Syst., 109, pp. 94-100 Toropova, A.P., Toropov, A.A., Martyanov, S.E., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna (2012) Chemom. Intell. Lab. Syst., 110, pp. 177-181 Brandrup, J., Immergut, E.H., Grulke, E.A., (1975) Polymer Handbook, , John Wiley and Sons http://www.insilico.eu/CORAL, (Accessed: 04-Aug-2014); Milano chemometrics and QSAR research group. VCCLAB, virtual computational chemistry laboratory, , http://michem.disat.unimib.it/chm, (Accessed: 24-July-2014) Katritzky group, , http://www.ark.chem.ufl.edu/, (Accessed: 24-July-2014) Rücker, C., Rücker, G., Meringer, M., Y-Randomization and its variants in QSPR/QSAR (2007) J. Chem. Inf. Model., 47, pp. 2345-2357 Gramatica, P., Principles of QSAR models validation: internal and external (2007) QSAR Comb. Sci., 26, pp. 694-701 Eriksson, L., Jaworska, J., Worth, A.P., Cronin, M.T., McDowell, R.M., Gramatica, P., Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs (2003) Environ. Health Perspect., 111, pp. 1361-1375 Mercader, A.G., Duchowicz, P.R., Fernández, F.M., Castro, E.A., Advances in the replacement and enhanced replacement method in QSAR and QSPR theories (2011) J. Chem. Inf. Model., 51, pp. 1575-1581 Roy, K., On some aspects of validation of predictive quantitative structure-activity relationship models (2007) Expert Opin. Drug Discovery, 2, pp. 1567-1577 |
| ISSN: | 01697439 |
| DOI: | 10.1016/j.chemolab.2014.11.008 |