Global and local QSPR models to predict supercooled vapour pressure for organic compounds

References

• P.C. van Noort, QSPRs for the estimation of subcooled liquid vapor pressures of polycyclic aromatic hydrocarbons, and of polychlorinated benzenes, biphenyls, dibenzo-p-dioxins, and dibenzofurans at environmentally relevant temperatures, Chemosphere 77 (2009), pp. 848–853.
   PubMed Web of Science ®Google Scholar
• M. Salahinejad and J. Ghasemi, 3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches, Ecotoxicol. Environ. Saf. 105 (2014), pp. 128–134.
   PubMed Web of Science ®Google Scholar
• Chayawan and Vikas, Externally predictive single-descriptor based QSPRs for physico-chemical properties of polychlorinated-naphthalenes: Exploring relationships of logS(W), logK(OA), and logK(OW) with electron-correlation, J. Hazard. Mater. 296 (2015), pp. 68–81.
   PubMed Web of Science ®Google Scholar
• M. Gavrilescu, Fate of pesticides in the environment and its bioremediation, Eng. Life Sci. 5 (2005), pp. 497–526.
   Web of Science ®Google Scholar
• T. Puzyn, N. Suzuki, and M. Haranczyk, How do the partitioning properties of polyhalogenated POPs change when chlorine is replaced with bromine?, Environ. Sci. Technol. 42 (2008), pp. 5189–5195.
   PubMed Web of Science ®Google Scholar
• T. Puzyn, A. Gajewicz, A. Rybacka, and M. Haranczyk, Global versus local QSPR models for persistent organic pollutants: Balancing between predictivity and economy, Struct. Chem. 22 (2011), pp. 873–884.
   Web of Science ®Google Scholar
• R. Balasubramanian and J. He, Fate and Transfer of Semivolatile Organic Compounds in a Multi-Compartment Environment, Urban Airborne Particulate Matter, Berlin, Springer, 2011, pp. 277–307.
   Google Scholar
• X. Zeng, Z. Wang, Z. Ge, and H. Liu, Quantitative structure–property relationships for predicting subcooled liquid vapor pressure (PL) of 209 polychlorinated diphenyl ethers (PCDEs) by DFT and the position of Cl substitution (PCS) methods, Atmos. Environ. 41 (2007), pp. 3590–3603.
   Web of Science ®Google Scholar
• H. Xiao and F. Wania, Is vapor pressure or the octanol–air partition coefficient a better descriptor of the partitioning between gas phase and organic matter?, Atmos. Environ. 37 (2003), pp. 2867–2878.
   Web of Science ®Google Scholar
• M. Bordbar, J. Ghasemi, A.Y. Faal, and R. Fazaeli, Chemometric modeling to predict aquatic toxicity of benzene derivatives using stepwise-multi linear regression and partial least square, Asian J. Chem. 25 (2013), pp. 331–342.
   Web of Science ®Google Scholar
• A. Gajewicz, M. Haranczyk, and T. Puzyn, Predicting logarithmic values of the subcooled liquid vapor pressure of halogenated persistent organic pollutants with QSPR: How different are chlorinated and brominated congeners?, Atmos. Environ. 44 (2010), pp. 1428–1436.
   Web of Science ®Google Scholar
• M. Salahinejad and E. Zolfonoun, QSAR studies of the dispersion of SWNTs in different organic solvents, J. Nanopart. Res. 15 (2013), pp. 1–9.
   Web of Science ®Google Scholar
• R. Vikas, Exploring the role of quantum chemical descriptors in modeling acute toxicity of diverse chemicals to Daphnia magna, J. Mol. Graph. Model. 61 (2015), pp. 89–101.
   PubMed Web of Science ®Google Scholar
• T. Puzyn, N. Suzuki, M. Haranczyk, and J. Rak, Calculation of quantum-mechanical descriptors for QSPR at the DFT level: Is it necessary?, J. Chem. Inf. Model. 48 (2008), pp. 1174–1180.
   PubMed Web of Science ®Google Scholar
• X.-L. Zeng, X.-L. Zhang, and Y. Wang, QSPR modeling of n-octanol/air partition coefficients and liquid vapor pressures of polychlorinated dibenzo-p-dioxins, Chemosphere 91 (2013), pp. 229–232.
   PubMed Web of Science ®Google Scholar
• C. Liang and D.A. Gallagher, QSPR prediction of vapor pressure from solely theoretically-derived descriptors, J. Chem. Inf. Comp. Sci. 38 (1998), pp. 321–324.
   Google Scholar
• G. Cruciani, P. Crivori, P.-A. Carrupt, and B. Testa, Molecular fields in quantitative structure–permeation relationships: The VolSurf approach, J. Mol. Struct.: THEOCHEM 503 (2000), pp. 17–30.
   Web of Science ®Google Scholar
• G. Cruciani, M. Pastor, and W. Guba, VolSurf: A new tool for the pharmacokinetic optimization of lead compounds, Eur. J. Pharm. Sci. 11 (2000), pp. S29–S39.
   PubMed Web of Science ®Google Scholar
• M. Sun, Y. Zheng, H. Wei, J. Chen, J. Cai, and M. Ji, enhanced replacement method-based quantitative structure–activity relationship modeling and support vector machine classification of 4-anilino-3-quinolinecarbonitriles as src kinase inhibitors, QSAR Combin. Sci. 28 (2009), pp. 312–324.
   Google Scholar
• A.G. Mercader, P.R. Duchowicz, F.M. Fernández, and E.A. Castro, Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories, J. Chem. Inf. Model. 50 (2010), pp. 1542–148.
   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
• 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.
   PubMed Web of Science ®Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q 2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
   PubMed Web of Science ®Google Scholar
• R. Mannhold, H. Kubinyi, G. Folkers, and G. Cruciani, Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction, John Wiley & Sons, 2006.
   Google Scholar
• A. Kungolos, Environmental Toxicology, WIT Press, Southampton, 2006.
   Google Scholar
• A. Tropsha, Best practices for QSAR model development, validation, and exploitation, Mol. Inform. 29 (2010), pp. 476–488.
   PubMed Web of Science ®Google Scholar
• K. Roy, I. Mitra, S. Kar, P.K. Ojha, R.N. Das, and H. Kabir, Comparative studies on some metrics for external validation of QSPR models, Chem. Inf. Model. 52 (2012), pp. 396–408.
   PubMed Web of Science ®Google Scholar
• S. Dimitrov, G. Dimitrova, T. Pavlov, N. Dimitrova, G. Patlewicz, J. Niemela, and O. Mekenyan, A stepwise approach for defining the applicability domain of SAR and QSAR models, Chem. Inf. Model. 45 (2005), pp. 839–849.
   PubMed Web of Science ®Google Scholar
• J. Jaworska, N. Nikolova-Jeliazkova, and T. Aldenberg, QSAR applicability domain estimation by projection of the training set descriptor space: A review, Altern. Lab. Anim. 33 (2005), pp. 445–459.
   PubMed Web of Science ®Google Scholar
• T. Puzyn and J. Falandysz, Prediction of log K OA, TC, and log PL for 281 chlorosubstituted pyrenes as the key parameters featuring environmental transport and fate of these compounds, J. Environ. Sci. Health A 38 (2003), pp. 1761–1780.
   Web of Science ®Google Scholar
• I. Jolliffe, Principal Component Analysis, New York, NY, Wiley Online Library, 2002.
   Google Scholar
• T. Puzyn and J. Falandysz, Computational estimation of logarithm of n-octanol/air partition coefficient and subcooled vapor pressures of 75 chloronaphthalene congeners, Atmos. Environ. 39 (2005), pp. 1439–1446.
   Web of Science ®Google Scholar
• M. Staikova, F. Wania, and D. Donaldson, Molecular polarizability as a single-parameter predictor of vapour pressures and octanol–air partitioning coefficients of non-polar compounds: A priori approach and results, Atmos. Environ. 38 (2004), pp. 213–225.
   Web of Science ®Google Scholar
• P. Geladi and B.R. Kowalski, Partial least-squares regression: A tutorial, Anal. Chim. Acta. 185 (1986), pp. 1–17.
   Web of Science ®Google Scholar
• Vikas and Chayawan, Externally predictive quantitative modeling of supercooled liquid vapor pressure of polychlorinated-naphthalenes through electron-correlation based quantum-mechanical descriptors, Chemosphere 95 (2014), pp. 448–454.
   Google Scholar
• Z.-Y. Wang, X.-L. Zeng, and Z.-C. Zhai, Prediction of supercooled liquid vapor pressures and n-octanol/air partition coefficients for polybrominated diphenyl ethers by means of molecular descriptors from DFT method, Sci. Total Environ. 389 (2008), pp. 296–305.
   PubMed Web of Science ®Google Scholar