QSPR studies on refractive indices of structurally heterogeneous polymers

Abstract 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.

[1]  Jozef Bicerano,et al.  Prediction of Polymer Properties , 1996 .

[2]  Eduardo A. Castro,et al.  QSAR on aryl-piperazine derivatives with activity on malaria , 2012 .

[3]  Pablo R Duchowicz,et al.  A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases. , 2011, Journal of molecular graphics & modelling.

[4]  Andrew G Mercader,et al.  Advances in the Replacement and Enhanced Replacement Method in QSAR and QSPR Theories , 2011, J. Chem. Inf. Model..

[5]  Gerta Rücker,et al.  y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..

[6]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[7]  Alan R. Katritzky,et al.  Correlation and Prediction of the Refractive Indices of Polymers by QSPR , 1998, J. Chem. Inf. Comput. Sci..

[8]  Jerzy Leszczynski,et al.  CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna , 2012 .

[9]  Mihai V. Putz,et al.  Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis , 2011, International journal of molecular sciences.

[10]  J. V. Julián-Ortiz,et al.  Prediction of Indices of Refraction and Glass Transition Temperatures of Linear Polymers by Using Graph Theoretical Indices , 2002 .

[11]  N. Hampp,et al.  High refractive index coumarin-based photorefractive polysiloxanes , 2013 .

[12]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[13]  Douglas M. Hawkins,et al.  Assessing Model Fit by Cross-Validation , 2003, J. Chem. Inf. Comput. Sci..

[14]  Alan Talevi,et al.  An integrated drug development approach applying topological descriptors. , 2012, Current computer-aided drug design.

[15]  Jerzy Leszczynski,et al.  Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector , 2007, Comput. Biol. Chem..

[16]  Jerzy Leszczynski,et al.  Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines , 2011 .

[17]  Emilio Benfenati,et al.  OCWLGI descriptors: theory and praxis. , 2013, Current computer-aided drug design.

[18]  Eduardo A. Castro,et al.  QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents , 2011 .

[19]  J. F. Gálvez,et al.  Prediction of Refractive Index of Polymers Using Artificial Neural Networks , 2010 .

[20]  R. Samuels.  Application of refractive index measurements to polymer analysis , 1981 .

[21]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[22]  Adrian Chiriac,et al.  Quantum-SAR Extension of the Spectral-SAR Algorithm. Application to Polyphenolic Anticancer Bioactivity , 2009, International journal of molecular sciences.

[23]  Jerzy Leszczynski,et al.  Predicting thermal conductivity of nanomaterials by correlation weighting technological attributes codes , 2007 .

[24]  Kunal Roy,et al.  On some aspects of validation of predictive quantitative structure–activity relationship models , 2007, Expert opinion on drug discovery.

[25]  W. Knoll,et al.  Interfaces and thin films as seen by bound electromagnetic waves. , 1998, Annual review of physical chemistry.

[26]  Paola Gramatica,et al.  Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. , 2003, Environmental health perspectives.

[27]  Pablo R Duchowicz,et al.  QSAR study for carcinogenicity in a large set of organic compounds. , 2012, Current drug safety.

[28]  Ekaterina Gordeeva,et al.  Traditional topological indexes vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research , 1993, J. Chem. Inf. Comput. Sci..

[29]  Qijin Zhang,et al.  Prediction of refractive indices of linear polymers by a four-descriptor QSPR model , 2004 .

[30]  Van Krevelen.  Properties of Polymers: Their Correlation with Chemical Structure; their Numerical Estimation and Prediction from Additive Group Contributions , 2009 .

[31]  Jerzy Leszczynski,et al.  Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. , 2012, Chemosphere.

[32]  A. Toropova,et al.  Prediction of heteroaromatic amine mutagenicity by means of correlation weighting of atomic orbital graphs of local invariants , 2001 .

[33]  B. Tang,et al.  Recent Progress in the Development of New Acetylenic Polymers , 2013 .

[34]  Stefanie A. Sydlik,et al.  Triptycene Polyimides: Soluble Polymers with High Thermal Stability and Low Refractive Indices , 2011 .

[35]  H. Kubinyi.  QSAR : Hansch analysis and related approaches , 1993 .

[36]  E Benfenati,et al.  Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: Using rare SMILES attributes to define the applicability domain. , 2008, Bioorganic & medicinal chemistry.