Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids
详细信息 查看全文
- 作者：Anna Rybinska ; Anita Sosnowska…
- 关键词：QSPR ; Geometry optimization ; Ionic liquids ; DFT ; PM7
- 刊名：Journal of Computer-Aided Molecular Design
- 出版年：2016
- 出版时间：February 2016
- 年：2016
- 卷：30
- 期：2
- 页码：165-176
- 全文大小：1,090 KB
- 参考文献：1.Wilkes JS (2002) A short history of ionic liquids—from molten salts to neoteric solvents. Green Chem 4(2):73–80. doi:10.1039/b110838g CrossRef
2.Bruzzone S, Chiappe C, Focardi SE, Pretti C, Renzi M (2011) Theoretical descriptor for the correlation of aquatic toxicity of ionic liquids by quantitative structure-toxicity relationships. Chem Eng J 175:17–23. doi:10.1016/J.Cej.08.073 CrossRef
3.Das RN, Roy K (2013) Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future. Mol Divers 17(1):151–196. doi:10.1007/s11030-012-9413-y CrossRef
4.Patel R, Kumari M, Khan AB (2014) Recent advances in the applications of ionic liquids in protein stability and activity: a review. Appl Biochem Biotechnol 172(8):3701–3720. doi:10.1007/s12010-014-0813-6 CrossRef
5.Paul TC, Morshed AKMM, Fox EB, Visser AE, Bridges NJ, Khan JA (2014) Thermal performance of ionic liquids for solar thermal applications. Exp Therm Fluid Sci 59:88–95. doi:10.1016/j.expthermflusci.2014.08.002 CrossRef
6.Zhang R, Wang CL, Yue QH, Zhou TC, Li N, Zhang HQ, Hao XK (2014) Ionic liquid foam floatation coupled with ionic liquid dispersive liquid-liquid microextraction for the separation and determination of estrogens in water samples by high-performance liquid chromatography with fluorescence detection. J Sep Sci 37(21):3133–3141. doi:10.1002/Jssc.201400568 CrossRef
7.Kapnissi-Christodoulou CP, Stavrou IJ, Mavroudi MC (2014) Chiral ionic liquids in chromatographic and electrophoretic separations. J Chromatogr A 1363:2–10. doi:10.1016/J.Chroma.05.059 CrossRef
8.Vogl T, Menne S, Balducci A (2014) Mixtures of protic ionic liquids and propylene carbonate as advanced electrolytes for lithium-ion batteries. Phys Chem Chem Phys 16(45):25014–25023. doi:10.1039/c4cp03830d CrossRef
9.Lu Y, Korf K, Kambe Y, Tu Z, Archer LA (2014) Ionic-liquid-nanoparticle hybrid electrolytes: applications in lithium metal batteries. Angew Chem Int Ed Engl 53(2):488–492. doi:10.1002/anie.201307137 CrossRef
10.Larsson K, Binnemans K (2014) Selective extraction of metals using ionic liquids for nickel metal hydride battery recycling. Green Chem 16(10):4595–4603. doi:10.1039/C3gc41930d CrossRef
11.Nasirpour N, Mousavi SM, Shojaosadati SA (2014) A novel surfactant-assisted ionic liquid pretreatment of sugarcane bagasse for enhanced enzymatic hydrolysis. Bioresour Technol 169:33–37. doi:10.1016/J.Biortech.06.023 CrossRef
12.Isik M, Sardon H, Mecerreyes D (2014) Ionic liquids and cellulose: dissolution, chemical modification and preparation of new cellulosic materials. Int J Mol Sci 15(7):11922–11940. doi:10.3390/Ijms150711922 CrossRef
13.Idris A, Vijayaraghavan R, Rana UA, Patti AF, MacFarlane DR (2014) Dissolution and regeneration of wool keratin in ionic liquids. Green Chem 16(5):2857–2864. doi:10.1039/C4gc00213j CrossRef
14.Muhammad N, Man Z, Bustam MA, Mutalib MIA (2012) Rafiq S (2013) Investigations of novel nitrile-based ionic liquids as pre-treatment solvent for extraction of lignin from bamboo biomass. J Ind Eng Chem 19(1):207–214. doi:10.1016/J.Jiec.08.003 CrossRef
15.Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Elsevier Academic Press, Amsterdam
16.Roy K, Kar S, Narayan Das R (2015) A primer on QSAR/QSPR modeling. Springer, Berlin. doi:10.1007/978-3-319-17281-1 CrossRef
17.Chen B-K, Liang M-J, Wu T-Y, Wang HP (2013) A high correlate and simplified QSPR for viscosity of imidazolium-based ionic liquids. Fluid Phase Equilib 350:37–42. doi:10.1016/j.fluid.2013.04.009 CrossRef
18.Bai LG, Zhu JQ, Chen BH (2011) Quantitative structure-property relationship study on heat of fusion for ionic liquids. Fluid Phase Equilib 312:7–13. doi:10.1016/J.Fluid.09.005 CrossRef
19.Gajewicz A, Haranczyk M, Puzyn T (2010) 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(11):1428–1436. doi:10.1016/j.atmosenv.2010.01.041 CrossRef
20.Peric B, Sierra J, Marti E, Cruanas R, Garau MA, Arning J, Bottin-Weber U, Stolte S (2013) (Eco)toxicity and biodegradability of selected protic and aprotic ionic liquids. J Hazard Mater 261:99–105. doi:10.1016/j.jhazmat.2013.06.070 CrossRef
21.Roy K, Das RN, Popelier PL (2014) Quantitative structure-activity relationship for toxicity of ionic liquids to Daphnia magna: aromaticity vs. lipophilicity. Chemosphere 112:120–127. doi:10.1016/j.chemosphere.2014.04.002 CrossRef
22.Li S-L, He M-Y, Du H-G (2011) 3D-QSAR studies on a series of dihydroorotate dehydrogenase inhibitors: analogues of the active metabolite of leflunomide. Int J Mol Sci 12(12):2982–2993. doi:10.3390/ijms12052982 CrossRef
23.Ruiz P, Myshkin E, Quigley P, Faroon O, Wheeler JS, Mumtaz MM, Brennan RJ (2013) Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗. SAR QSAR Environ Res 24(5):393–416. doi:10.1080/1062936x.2013.781537 CrossRef
24.Cronin MTD (2010) Quantitative Structure-Activity Relationships (QSARs)—applications and methodology. In: Puzyn T, Leszczynski J, Cronin MTD (eds) Recent advances in QSAR studies. Methods and applications. Challenges and advances in computational chemistry and physics, vol 8. Springer, New York, pp 3–11. doi:10.1007/978-1-4020-9783-6
25.Jensen F (1999) Introduction to computational chemistry. Wiley, New York
26.Stewart JJ (2004) Comparison of the accuracy of semiempirical and some DFT methods for predicting heats of formation. J Mol Model 10(1):6–12. doi:10.1007/s00894-003-0157-6 CrossRef
27. Young DC (2001) Computational chemistry: a practical guide for applying techniques to real-world problems. Wiley-Interscience, New York
28.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 Model 42(1):71–74. doi:10.1021/ci0100503 CrossRef
29.Ma S, Lv M, Zhang X, Zhai H, Lv W (2015) Computational study of the effects of cations and anions to the cytotoxicity of diverse ionic liquids by supervised machine learning. Chemometrics Intell Lab Syst 144:138–147. doi:10.1016/j.chemolab.2015.03.014 CrossRef
30.Yu G, Wen L, Zhao D, Asumana C, Chen X (2013) QSPR study on the viscosity of bis(trifluoromethylsulfonyl)imide-based ionic liquids. J Mol Liq 184:51–59. doi:10.1016/j.molliq.2013.04.021 CrossRef
31.Bai L, Zhu J, Chen B (2011) Quantitative structure–property relationship study on heat of fusion for ionic liquids. Fluid Phase Equilib 312:7–13. doi:10.1016/j.fluid.2011.09.005 CrossRef
32.Dong Q, Muzny CD, Kazakov A, Diky V, Magee JW, Widegren JA, Chirico RD, Marsh KN, Frenkel M (2007) ILThermo: a free-access web database for thermodynamic properties of ionic liquids. J Chem Eng Data 52(4):1151–1159. doi:10.1021/Je700171f CrossRef
33.Dennington R, Keith T, Millam J (2009) GaussView, 5th edn. Semichem Inc., Kansas
34.Stewart JJP (2012) MOPAC2012. Stewart Computational Chemistry, Colorado Springs
35.Wan J, Zhang L, Yang G, Zhan CG (2004) Quantitative Structure-Activity Relationship for cyclic imide derivatives of protoporphyrinogen oxidase inhibitors: a study of quantum chemical descriptors from density functional theory. J Chem Inf Model 44(6):2099–2105. doi:10.1021/ci049793p CrossRef
36.Rasulev BF, Abdullaev ND, Syrov VN, Leszczynski J (2005) A Quantitative Structure-Activity Relationship (QSAR) study of the antioxidant activity of flavonoids. QSAR Comb Sci 24(9):1056–1065. doi:10.1002/qsar.200430013 CrossRef
37.Puzyn T, Suzuki N, Haranczyk M, Rak J (2008) Calculation of quantum-mechanical descriptors for QSPR at the DFT level: is it necessary? J Chem Inf Model 48(6):1174–1180. doi:10.1021/Ci800021p CrossRef
38.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(8):1128–1134. doi:10.1016/j.chemosphere.2009.01.019 CrossRef
39.Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE, Fo Ogliaro, Bearpark MJ, Heyd J, Brothers EN, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam NJ, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ăd, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09. Gaussian Inc, Wallingford
40.Talete (2014) Dragon (software for molecular descriptor calculation), 6.0, Milano. http://www.talete.mi.it/
41.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(4):795–804. doi:10.1007/s11224-011-9757-4 CrossRef
42.Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34(24):2121–2132. doi:10.1002/Jcc.23361 CrossRef
43.Gramatica P, Cassani S, Chirico N (2014) QSARINS-chem: insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem 35(13):1036–1044. doi:10.1002/jcc.23576 CrossRef
44.OECD (2004) The report from the expert group on (quantitative) structure activity relationship [(Q)SARs] on the principles for the validation of (Q)SARs. Series on testing and assessment No. 49 (ENV/JM/MONO(2004)24). Organisation of Economic Cooperation and Development, Paris
45.Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17(5):4791–4810. doi:10.3390/molecules17054791 CrossRef
46.Gramatica P (2013) On the development and validation of QSAR models. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol 930., Methods in molecular biologyHumana Press, New York, pp 499–526. doi:10.1007/978-1-62703-059-5_21 CrossRef
47.Sahlin U, Jeliazkova N, Oberg T (2014) Applicability domain dependent predictive uncertainty in QSAR regressions. Mol Inform 33(1):26–35. doi:10.1002/Minf.201200131 CrossRef
48.Netzeva T, Worth AP, Aldenberg T, Benigni R, Cronin M, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova-Jeliazkova N, Patlewicz G, Perkins R, Roberts DW, Schultz TW, Stanton DT, van de Sandt J, Tong W, Veith G, Yang C (2005) Current status of methods for defining the applicability domain of (quantitative) structure–activity relationships. The report and recommendations of ECVAM workshop 52. alternatives to laboratory animals, vol 33
49.Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E (2012) QSAR modeling is not “push a button and find a correlation”: a case study of toxicity of (benzo-)triazoles on algae. Mol Inf 31(11–12):817–835. doi:10.1002/minf.201200075 CrossRef
50.Gramatica P (2010) Chemometric methods and theoretical molecular descriptors in predictive QSAR modeling of the environmental behavior of organic pollutants. In: Puzyn T, Leszczynski J, Cronin MTD (eds) Recent advances in QSAR studies. Methods and applications, vol 8. Springer, New York, pp 327–366. doi:10.1007/978-1-4020-9783-6 CrossRef
51.Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chem Intell Lab Syst 145:22–29. doi:10.1016/j.chemolab.2015.04.013 CrossRef
52.Gramatica P, Giani E, Papa E (2007) Statistical external validation and consensus modeling: a QSPR case study for Koc prediction. J Mol Graph Model 25(6):755–766. doi:10.1016/j.jmgm.2006.06.005 CrossRef
53.Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51(9):2320–2335. doi:10.1021/ci200211n CrossRef
54.Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN (2013) 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(12):1071–1082. doi:10.1002/jcc.23231 CrossRef
55.Toth G, Bodai Z, Heberger K (2013) Estimation of influential points in any data set from coefficient of determination and its leave-one-out cross-validated counterpart. J Comput Aided Mol Des 27(10):837–844. doi:10.1007/S10822-013-9680-4 CrossRef
56.Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701. doi:10.1002/Qsar.200610151 CrossRef
57.Devinyak O, Havrylyuk D, Lesyk R (2014) 3D-MoRSE descriptors explained. J Mol Graph Model 54:194–203. doi:10.1016/j.jmgm.2014.10.006 CrossRef
58.Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. Wiley, WeinheimCrossRef
59.Rinnan Å, Christensen NJ, Engelsen SB (2009) How the energy evaluation method used in the geometry optimization step affect the quality of the subsequent QSAR/QSPR models. J Comput Aided Mol Des 24(1):17–22. doi:10.1007/s10822-009-9308-x CrossRef
60.Roy K, Popelier PLA (2008) Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors. Bioorg Med Chem Lett 18(8):2604–2609. doi:10.1016/j.bmcl.2008.03.035 CrossRef
61.Kar S, Harding AP, Roy K, Popelier PLA (2010) QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis. SAR QSAR Environ Res 21(1–2):149–168. doi:10.1080/10629360903568697 CrossRef - 作者单位：Anna Rybinska (1)
Anita Sosnowska (1)
Maciej Barycki (1)
Tomasz Puzyn (1)
1. Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdańsk, Poland - 刊物类别：Chemistry and Materials Science
- 刊物主题：Chemistry
Physical Chemistry
Computer Applications in Chemistry
Animal Anatomy, Morphology and Histology - 出版者：Springer Netherlands
- ISSN：1573-4951
文摘
Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids’ (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities (\({\text{Q}}_{\text{EXT}}^{2}\) = 0.87). However, PM7-based model has comparable values of quality parameters (\({\text{Q}}_{\text{EXT}}^{2}\) = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.