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Articles

QSPR modelling for prediction of glass transition temperature of diverse polymers

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Pages 935-956 | Received 03 Sep 2018, Accepted 10 Oct 2018, Published online: 05 Nov 2018

References

  • H. Namazi, Polymers in our daily life, Bioimpacts 7 (2017), pp. 73–74.
  • M. Rose, Nanoporous polymers: Bridging the gap between molecular and solid catalysts? ChemCatChem. 6 (2014), pp. 1166–1182.
  • X. Du, Y. Sun, B. Tan, Q. Teng, X. Yao, C. Su, and W. Wang, Tröger’s base-functionalised organic nanoporous polymer for heterogeneous catalysis, Chem. Commun. 46 (2010), pp. 970–972.
  • K. Jiang, T. Fei, and T. Zhang, Humidity sensing properties of LiCl-loaded porous polymers with good stability and rapid response and recovery, Sens. Actuators B Chem. 199 (2014), pp. 1–6.
  • K. Jiang, D. Kuang, T. Fei, and T. Zhang, Preparation of lithium-modified porous polymer for enhanced humidity sensitive properties, Sens. Actuators B Chem. 203 (2014), pp. 752–758.
  • B. Li, X. Yang, L. Xia, M.I. Majeed, and B. Tan, Hollow microporous organic capsules, Sci. Rep. 3 (2013), p. 2128.
  • M.R. Aguilar and J.S. Román, Smart Polymers and their Applications, Elsevier Science, Amsterdam, 2014.
  • D. van Krevelen, Properties of Polymer: Their Correlation with Chemical Structure; Their Numerical Estimation and Prediction from Additive Group Contributions, Elsevier, Amsterdam, 1990.
  • J. Meyer, Glass transition temperature as a guide to selection of polymers suitable for PTC materials, Polym. Eng. Sci. 13 (1973), pp. 462–468.
  • J. Bicerano, Prediction of the properties of polymers from their structures, J. Macromol. Sci. Polymer Rev. 36 (1996), pp. 161–196.
  • D.J. Livingstone and A.M. Davis, Drug Design Strategies: Quantitative Approaches, Royal Society of Chemistry, London, 2011.
  • P. Gramatica, E. Giani, and E. Papa, Statistical external validation and consensus modeling: A QSPR case study for Koc prediction, J. Molec. Graph. Model. 25 (2007), pp. 755–766.
  • X. Yu, X. Wang, H. Wang, A. Liu, and C. Zhang, Prediction of the glass transition temperatures of styrenic copolymers using a QSPR based on the DFT method, J. Mol. Struct. Theochem. 766 (2006), pp. 113–117.
  • L.S. Petrosyan, S. Kar, J. Leszczynski, and B. Rasulev, Exploring simple, interpretable, and predictive QSPR model of fullerene C60 solubility in organic solvents, J. Nanotoxicol. Nanomed. 2 (2017), pp. 28–43.
  • J.C. Dearden, The history and development of quantitative structure–activity relationships (QSARs), Int. J.  Quant. Struct.-Prop. Relat. 1(2016), pp. 1–44.
  • A.R. Katritzky, S. Sild, V. Lobanov, and M. Karelson, Quantitative structure−property relationship (QSPR) correlation of glass transition temperatures of high molecular weight polymers, J. Chem. Inf. Comput. Sci. 38 (1998), pp. 300–304.
  • R. García-Domenech and J. de Julián-Ortiz, Prediction of indices of refraction and glass transition temperatures of linear polymers by using graph theoretical indices, J. Phys. Chem. B 106 (2002), pp. 1501–1507.
  • X. Yu and X. Huang, A quantitative relationship between Tgs and chain segment structures of polystyrenes, Polímeros 27 (2017), pp. 68–74.
  • J. Bicerano, Prediction of Polymer Properties, CRC Press, Boca Raton, 2002.
  • A.R. Katritzky, P. Rachwal, K.W. Law, M. Karelson, and V.S. Lobanov, Prediction of polymer glass transition temperatures using a general quantitative structure−property relationship treatment, J. Chem. Inf. Comput. Sci. 36 (1996), pp. 879–884.
  • C. Cao and Y. Lin, Correlation between the glass transition temperatures and repeating unit structure for high molecular weight polymers, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 643–650.
  • B.E. Mattioni and P.C. Jurs, Prediction of glass transition temperatures from monomer and repeat unit structure using computational neural networks, J. Chem. Inf. Comput. Sci. 42 (2002), pp. 232–240.
  • X. Chen, L. Sztandera, and H.M. Cartwright, A neural network approach to prediction of glass transition temperature of polymers, Int. J. Intell. Syst. 23 (2008), pp. 22–32.
  • A.G. Mercader and P.R. Duchowicz, Encoding alternatives for the prediction of polyacrylates glass transition temperature by quantitative structure–property relationships, Mater. Chem. Phys. 172 (2016), pp. 158–164.
  • A.G. Mercader, D.E. Bacelo, and P.R. Duchowicz, Different encoding alternatives for the prediction of halogenated polymers glass transition temperature by quantitative structure–property relationships, Int. J. Polym. Anal. Ch. 22 (2017), pp. 639–648.
  • M. Chen, F. Jabeen, B. Rasulev, M. Ossowski, and P. Boudjouk, A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers, J. Polym. Sci. B Polym. Phys. 56 (2018), pp. 877–885.
  • J. Brandrup, E. Immergut, and W. McDowell, Polymer Handbook, Wiley, New York, 1975.
  • A. Mauri, V. Consonni, M. Pavan, and R. Todeschini, Dragon software: An easy approach to molecular descriptor calculations, Match 56 (2006), pp. 237–248.
  • C.W. Yap, PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474.
  • D. Rogers and A.J. Hopfinger, Application of genetic function approximation to quantitative structure–activity relationships and quantitative structure–property relationships, J. Chem. Inf. Comput. Sci. 34 (1994), pp. 854–866.
  • Y. Fan, L.M. Shi, K.W. Kohn, Y. Pommier, and J.N. Weinstein, Quantitative structure–antitumor activity relationships of camptothecin analogues: Cluster analysis and genetic algorithm-based studies, J. Med. Chem. 44 (2001), pp. 3254–3263.
  • R.B. Darlington, Regression and Linear Models, McGraw-Hill, New York, 1990.
  • D. Baumann and K. Baumann, Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation, J. Cheminform. 6 (2014), p. 47.
  • K. Roy and P. Ambure, The “double cross-validation” software tool for MLR QSAR model development, Chemom. Intell. Lab. Syst. 159 (2016), pp. 108–126.
  • S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: A basic tool of chemometrics, Chemom. Intell. Lab. Syst. 58 (2001), pp. 109–130.
  • A. Ghose and V. Viswanadhan, Combinatorial Library Design and Evaluation: Principles, Software, Tools, and Applications in Drug Discovery, Taylor & Francis, New York, 2001.
  • K. Roy, P. Chakraborty, I. Mitra, P.K. Ojha, S. Kar, and R.N. Das, Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data, J. Comput. Chem. 34 (2013), pp. 1071–1082.
  • A. Golbraikh and A. Tropsha, Beware of q2! J. Mol. Graph. Model. 20 (2002), pp. 269–276.
  • K. Roy, P. Ambure, S. Kar, and P.K. Ojha, Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J. Chemom. 32 (2018), pp. e2992.
  • K. Roy, R.N. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab. Syst. 152 (2016), pp. 18–33.
  • V. Consonni and R. Todeschini, New spectral indices for molecule description, MATCH 60 (2008) pp. 3–14.
  • J. Danielson, A. Jones, J. Gosselin, M. Natisin, and C. Surko, Interplay between permanent dipole moments and polarizability in positron-molecule binding, Phys. Rev. A 85 (2012), p. 022709.
  • V.H. Dalvi and P.J. Rossky, Molecular origins of fluorocarbon hydrophobicity, Proc. Natl. Acad. Sci. 107 (2010), pp. 13603–13607.
  • T. Luechtefeld, A. Maertens, D.P. Russo, C. Rovida, H. Zhu, and T. Hartung, Analysis of public oral toxicity data from REACH registrations 2008–2014, Altex 33 (2016), pp. 111–122.
  • G. OmPraba and D. Velmurugan, Quantitative structure–activity relationship (QSAR) analysis of a series of indole analogues as inhibitor for human group V secretory phospholipase A₂, Indian J. Biochem. Biophys. 43 (2006), pp. 154–159.
  • R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing/Volume II: Appendices, References, John Wiley & Sons, New York, 2009.
  • T. Kureha, Y. Nishizawa, and D. Suzuki, Controlled separation and release of organoiodine compounds using poly(2-methoxyethyl acrylate)-analogue microspheres, ACS Omega 2 (2017), pp. 7686–7694.
  • L. Nadareishvili, Polymers and Polymeric Materials for Fiber and Gradient Optics, VSP, Tokyo, 2002.
  • K. Roy, Quantitative Structure–Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, IGI Global, Hershey, PA, 2015.
  • R.O. Ebewele, Polymer Science and Technology, CRC Press, New York, 2000.
  • S. Riahi, E. Pourbasheer, R. Dinarvand, M.R. Ganjali, and P. Norouzi, QSAR study of 2‐(1‐propylpiperidin‐4‐yl)‐1H‐benzimidazole‐4‐carboxamide as PARP inhibitors for treatment of cancer, Chem. Biol Drug Des. 72 (2008), pp. 575–584.
  • B. De, I. Adhikari, A. Nandy, A. Saha, and B.B. Goswami, In silico modelling of azole derivatives with tyrosinase inhibition ability: Application of the models for activity prediction of new compounds, Comput. Biol. Chem. 74 (2018), pp. 105–114.
  • L. Saghaie, M. Shahlaei, A. Fassihi, A. Madadkar‐Sobhani, M.B. Gholivand, and A. Pourhossein, QSAR analysis for some diaryl‐substituted pyrazoles as ccr2 inhibitors by ga‐stepwise MLR, Chem. Biol. Drug Des. 77 (2011), pp. 75–85.
  • K. Roy and G. Ghosh, QSTR with extended topochemical atom indices. 2. Fish toxicity of substituted benzenes, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 559–567.
  • U. SIMCA-P, 10.0, https://umetrics.com/ (last accessed on 25 Oct 2018), Umea, Sweden, 2002.
  • N. Akarachantachote, S. Chadcham, and K. Saithanu, Cutoff threshold of variable importance in projection for variable selection, Int. J. Pure Appl. Math. 94 (2014), pp. 307–322.
  • K. Roy, P. Ambure, and S. Kar, How precise are our quantitative structure–activity relationship derived predictions for new query chemicals? ACS Omega 3 (2018) pp. 11392–11406.
  • S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: A basic tool of chemometrics, ‎Chemom. Intell. Lab. Syst. 58 (2001), pp. 109–130.

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