Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach
详细信息 查看全文
- 作者：Marquita Watkins ; Natalia Sizochenko ; Bakhtiyor Rasulev…
- 关键词：Melting point ; POPs ; Organic pollutants ; Partial least squares ; QSPR ; Random forest
- 刊名：Journal of Molecular Modeling
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：22
- 期：3
- 全文大小：1,764 KB
- 参考文献：1.de Lima Ribeiro FA, Ferreira M (2003) QSPR models of boiling point, octanol–water partition coefficient and retention time index of polycyclic aromatic hydrocarbons. J Mol Struc 663:109–126CrossRef
2.Nadal M, Espinosa G, Schuhmacher M, Domingo JL (2004) Patterns of PCDDs and PCDFs in human milk and food and their characterization by artificial neural networks. Chemosphere 54:1375–1382CrossRef
3.Ding G, Chen J, Qiao X, Huang L, Lin J, Chen X (2006) Quantitative relationships between molecular structures, environmental temperatures and solid vapor pressures of PCDD/Fs. Chemosphere 62:1057–1063CrossRef
4.Xu HY, Zou JW, Yu QS, Wang YH, Zhang JY, Jin HX (2007) QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers. Chemosphere 66:1998–2010CrossRef
5.Ritter L, Solomon KR, Forget J, Stemeroff M, O'Leary C (1995) Persistent organic pollutants: an assessment report, International Programme on Chemical Safety. World Health Organization, Canada
6.Balk S, Carpenter DO, Corra L, Diaz-Barriga MFR, Sly P, Ehrenstein OSV, Tirado MC (2010) Persistent organic pollutants: impact on child health. Geneva, Switzerland.
7.Nicolopoulou-Stamati P, Pitsos MA (2001) The Impact of endocrine disrupters on the female reproductive system. Hum Reprod Update 7:323–330CrossRef
8.Katritzky AR, Maran U, Lobanov VS, Karelson M (2000) Structurally diverse quantitative structure–property relationship correlations of technologically relevant physical properties. J Chem Inf Comput Sci 40:1–18CrossRef
9.Vasseur P, Cossu-Leguille C (2006) Linking molecular interactions to consequent effects of persistent organic pollutants (POPs) upon populations. Chemosphere 62:1033–1042CrossRef
10.Katritzky AR, Maran U, Karelson M, Lobanov VS (1997) Prediction of melting points for the substituted benzenes: a QSPR approach. J Chem Inf Comp Sci 37:913–919
11.Katritzky AR, Lomaka A, Petrukhin R, Jain R, Karelson M, Visser AE, Rogers RD (2002) QSPR correlation of the melting point for pyridinium bromides, potential ionic liquids. J Chem Inf Comp Sci 42:71–74CrossRef
12.Schultz TW, Cronin MTD, Walker JD, Aptula AO (2003) Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective. J Mol Struct 622:1–22CrossRef
13.Bolboaca SD, Jäntschi L (2013) Quantitative structure-activity relationships: linear regression modelling and validation strategies by example. Biomath 2(1):1–11CrossRef
14.Asadollahi T, Dadfarnia S, Shabani AM, Ghasemi JB, Sarkhosh M (2011) QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening. Molecules 16:1928–1955CrossRef
15.Gramatica P, Consolaro F, Pozzi S (2001) QSAR approach to POPs screening for atmospheric persistence. Chemosphere 43:655–664CrossRef
16.Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High T Scr 9:213–228
17.OECD (2014) A guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. http://www.oecd.org/dataoecd/33/37/37849783.pdf (accessed July 13)
18.Bhhatarai B, Teetz W, Liu T, Öberg T, Jeliazkova N, Kochev N, Pukalov O, Tetko IV, Kovarich S, Papa E, Gramatica P (2011) CADASTER QSPR models for predictions of melting and boiling points of perfluorinated chemicals. Mol Inf 30:189–204CrossRef
19.Trohalaki S, Pachter R, Drake GW, Hawkins T (2005) Quantitative structure–property relationships for melting points and densities of ionic liquids. Energy Fuels 19:279–284CrossRef
20.Katritzky AR, Jain R, Lomaka A, Petrukhin R, Maran U, Karelson M (2001) Perspective on the relationship between melting points and chemical structure. Cryst Growth Des 1:261–265CrossRef
21.Godavarthy SS, Robinson RL Jr, Gasem KAM (2006) An improved structure–property model for predicting melting-point temperatures. Ind Eng Chem Res 45:5117–5126CrossRef
22.Murugan R, Grendze MP, Toomey J, Katrizky A, Karelson M, Lobanov V, Rachwal P (1994) Predicting physical properties from molecular structure. Chemtech, Washington DC 24:17–17
23.Hanson M, Rouvray D (1987) The use of topological indices to estimate the melting points of organic molecules. Graph Theor Topol Chem 51:201–208
24.Abramowitz R, Yalkowsky S (1990) Melting point, boiling point, and symmetry. Pharm Res 7:942–947CrossRef
25.Dearden JC (1991) The QSAR prediction of melting point, a property of environmental relevance. Sci Total Environ 109–110:59–68CrossRef
26.Charton M, Charton B (1994) Quantitative description of structural effects on melting points of substituted alkanes. J Phys Org Chem 7:196–206CrossRef
27.Gramatica P, Navas N, Todeschini R (1998) 3D-modelling and prediction by WHIM descriptors. Part 9. Chromatographic relative retention time and physico-chemical properties of polychlorinated biphenyls (PCBs). Chemomet Intell Lab Syst 40(1):53–56CrossRef
28.Kušić H, Rasulev B, Leszczynska D, Leszczynski J, Koprivanac N (2009) Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: a QSAR study. Chemosphere 75:1128–1134CrossRef
29.Puzyn T, Gajewicz A, Rybacka A, Haranczyk M (2011) Global versus local QSPR models for persistent organic pollutants: balancing between predictivity and economy. Struct Chem 22:873–884CrossRef
30.Howard PH, Muir DC (2011) Identifying new persistent and bioaccumulative organics among chemicals in commerce II: pharmaceuticals. Environ Sci Tech 45:6938–6946CrossRef
31.Gajewicz A, Haranczyk M, Puzyn T (2011) 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 44:1428–1436
32.Odziomek K, Gajewicz A, Haranczyk M, Puzyn T (2013) Reliability of environmental fate modeling results for POPs based on various methods of determining the air/water partition coefficient (log Kaw). Atmos Environ 73:177–184CrossRef
33.Puzyn T, Mostrag-Szlichtyng A, Gajewicz A, Skrzyński M, Worth AP (2011) Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models. Struct Chem 22:795–804CrossRef
34.Puzyn T, Mostrag-Szlichtyng A, Falandysz J, Kholod Y, Leszczynski J (2009) Predicting water solubility of congeners: chloronaphthalenes—a case study. J Hazard Mat 170:1014–1022CrossRef
35.Coleman WF, Arumainayagam CR (1998) HyperChem 5 (by Hypercube Inc). J Chem Ed 75:416
36.Vargyas M, Papp J, Csizmadia F, Csepregi S, Papp A, Vadasz P (2008) Maximum common substructure based hierarchical clustering, Noordwijkerhout, Netherlands
37.Mauri A, Consonni V, Pavan M, Todeschini R (2006) Dragon software: an easy approach to molecular descriptor calculations. Match 56: 237–248
38.EigenVector Research (2014) EigenVector Research, I. PLS Toolbox, 7.8.2. http://www.eigenvector.com/
39.MATLAB (2014) MATLAB (R2014a), version 8.3. Natick, MA
40.Hasegawa K, Funatsu K (2000) Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships. SAR QSAR Environ Res 11:189–209
41.Liaw A, News MWR (2002) Classification and regression by random forest. R News 2:18–22
42.Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRef
43.Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRef
44.Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276CrossRef
Natalia Sizochenko (1)
Bakhtiyor Rasulev (1) (2)
Jerzy Leszczynski (1)
1. Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, P.O. Box: 17910, Jackson, MS, USA
2. Center for Computationally Assisted Science and Technology, North Dakota State University, Fargo, ND, USA
Computer Applications in Chemistry
Biomedicine
Molecular Medicine
Health Informatics and Administration
Life Sciences
Computer Application in Life Sciences
文摘
The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure—property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed. Keywords Melting point POPs Organic pollutants Partial least squares QSPR Random forest