QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors

References

• C. Scharsich, R.H. Lohwasser, M. Sommer, U. Asawapirom, U. Scherf, M. Thelakkat, D. Nieher, and A. Köhler, Control of aggregate formation in poly (3-hexylthiophene) by solvent, molecular weight, and synthetic method, J. Polym. Sci., Part B: Polym. Phys. 50 (2012), pp. 442–453.
   Web of Science ®Google Scholar
• H. Ma, A.K.Y. Jen, and L.R. Dalton, Polymer-based optical waveguides: Materials, processing, and devices, Adv. Mater. 14 (2002), pp. 1339–1365.
   Web of Science ®Google Scholar
• H. Zhou, L. Yang, and W. You, Rational design of high performance conjugated polymers for organic solar cells, Macromolecules 45 (2012), pp. 607–632.
   Web of Science ®Google Scholar
• P.D. Cunningham, N.N. Valdes, F.A. Vallejo, L.M. Hayden, B. Polishak, X.H. Zhou, and R.J. Twieg, Broadband terahertz characterization of the refractive index and absorption of some important polymeric and organic electro-optic materials, J. Appl. Phys. 109 (2011), pp. 043505–043505.
   Web of Science ®Google Scholar
• Z.B. Henson, K. Müllen, and G.C. Bazan, Design strategies for organic semiconductors beyond the molecular formula, Nat. Chem. 4 (2012), pp. 699–704.
   PubMed Web of Science ®Google Scholar
• D.W. van Krevelen and K. te Nijenhuis, Properties of Polymers: Their Correlation with Chemical Structure; Their Numerical Estimation and Prediction from Additive Group Contributions, Elsevier, New York, 2009.
   Google Scholar
• A.R. Katritzky, V.S. Lobanov, and M. Karelson, QSPR: The correlation and quantitative prediction of chemical and physical properties from structure. Chem. Soc. Rev. 24 (1995), pp. 279–287.
   Web of Science ®Google Scholar
• A.R. Katritzky, M. Karelson, and V.S. Lobanov, QSPR as a means of predicting and understanding chemical and physical properties in terms of structure, Pure Appl. Chem. 69 (1997), pp. 245–248.
   Web of Science ®Google Scholar
• R. Kunal, S. Kar, and R. Narayan Das, A Primer on QSAR/QSPR Modeling: Fundamental Concepts, Springer, Berlin, 2015.
   Google Scholar
• A.R. Katritzky and E.V. Goordeva, Traditional topological indices vs. electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research, J. Chem. Inf. Comput. Sci. 33 (1993), pp. 835–857.
   PubMedGoogle Scholar
• M.V.E. Diudea, QSPR/QSAR Studies by Molecular Descriptors, Nova Science Publishers, New York, 2001.
   Google Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics (Methods and Principles in Medicinal Chemistry), Wiley-VCH, Weinheim, 2009.
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graphics Modell. 20 (2002), pp. 269–276.
   Google Scholar
• D.M. Hawkins, S.C. Basak, and D. Mills, Assessing model fit by cross validation, J. Chem. Inf. Model. 43 (2003), pp. 579–586.
   Web of Science ®Google Scholar
• M.V. Putz, A.-M. Putz, M. Lazea, L. Ienciu, and A. Chiriac, Quantum-SAR extension of the spectral-SAR algorithm. Application to polyphenolic anticancer bioactivity, Int. J. Mol. Sci. 10 (2009), pp. 1193–1214.
   PubMed Web of Science ®Google Scholar
• M.V. Putz, C. Ionascu, A.M. Putz, and V. Ostafe, Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis, Int. J. Mol. Sci. 12 (2011), pp. 5098–5134.
   PubMed Web of Science ®Google Scholar
• Z. Ha, Z. Ring, and S. Liu, Quantitative structure–property relationship (QSPR) models for boiling points, specific gravities, and refraction indices of hydrocarbons, Energy Fuels 19 (2005), pp. 152–163.
   Web of Science ®Google Scholar
• W. Liu, P. Yi, and Z. Tang, QSPR models for various properties of polymethacrylates based on quantum chemical descriptors, QSAR Comb. Sci. 25 (2006), pp. 936–943.
   Google Scholar
• A.A. Toropov and A.P. Toropova, QSPR modeling of alkanes properties based on graph of atomic orbitals. J. Mol. Struc.: THEOCHEM 637 (2003), pp. 1–10.
   Web of Science ®Google Scholar
• A.R. Katritzky, S. Sild, and M. Karelson, General quantitative structure–property relationship treatment of the refractive index of organic compounds, J. Chem. Inf. Comput. Sci. 38 (1998), pp. 840–844.
   Google Scholar
• P.R. Duchowicz, N.C. Comelli, E.V. Ortiz, and E.A. Castro, QSAR study for carcinogenicity in a large set of organic compounds, Curr. Drug Saf. 7 (2012), pp. 282–288.
   PubMedGoogle Scholar
• A. Talevi, C.L. Bellera, M.D. Ianni, P.R. Duchowicz, L.E. Bruno-Blanch, and E.A. Castro, An integrated drug development approach applying topological descriptors, Curr. Comput. Aided Drug Des. 8 (2012), pp. 172–181.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, E. Benfenati, and G. Gini, OCWLGI descriptors: Theory and praxis, Curr. Comput. Aided Drug Des. 9 (2013), pp. 226–232.
   PubMed Web of Science ®Google Scholar
• J. Brandrup, E.H. Immergut, and E.A. Grulke (eds.), Polymer Handbook, 4th ed., Wiley-Interscience, New York, 1999.
   Google Scholar
• Coral, http://www.insilico.eu/coral.
   Google Scholar
• A.P. Toropova, A.A. Toropov, S.E. Martyanov, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski, CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna, Chemometr. Intell. Lab. Syst. 110 (2012), pp. 177–181.
   Web of Science ®Google Scholar
• C. Rücker, G. Rücker, and M. Meringer, Y-Randomization and its variants in QSPR/QSAR, J. Chem. Inf. Model. 47 (2007), pp. 2345–2357.
   PubMed Web of Science ®Google Scholar
• A.G. Mercader, P.R. Duchowicz, F.M. Fernández, and E.A. Castro, Advances in the replacement and enhanced replacement method in QSAR and QSPR theories, J. Chem. Inf. Model. 51 (2011), pp. 1575–1581.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, E. Benfenati, O. Nicolotti, A. Carotti, K. Nesmerak, A.M. Veselinović, J.B. Veselinović, P.R. Duchowicz, D. Bacelo, E.A. Castro, B.F. Rasulev, D. Leszczynska, and J. Leszczynski, Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment, in QSPR/QSAR Analyses by Means of the CORAL Software: Results, Challenges, Perspectives, K. Roy, ed., Hershey, IGI Global, 2015, pp. 560–585.
   Google Scholar
• R.P. Verma and C. Hansch, An approach toward the problem of outliers in QSAR, Bioorg. Med. Chem. 13 (2005), pp. 4597–4621.
   PubMed Web of Science ®Google Scholar
• K. Roy, On some aspects of validation of predictive quantitative structure-activity relationship models, Expert. Opin. Drug. Discov. 2 (2007), pp. 1567–1577.
   PubMed Web of Science ®Google Scholar