Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

doi:10.3866/PKU.WHXB201701163

Abstract

Abstract:

The purpose of this paper is to present a novel way to building quantitative structure-propertyrelationship (QSPR) models for predicting the gas-to-benzene solvation enthalpy (ΔH_Solv) of 158 organiccompounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptorswere calculated for each compounds using dragon package. The variable selection technique of enhancedreplacement method (ERM) was employed to select optimal subset of descriptors. Our investigation revealsthat the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact andthat ERM method is unable to model the solvation enthalpy accurately. The standard error value of predictionset for support vector machine (SVM) is 1.681 kJ·mol^-1 while it is 4.624 kJ·mol^-1 for ERM. The resultsestablished that the calculated ΔH_Solv values by SVM were in good agreement with the experimental ones, andthe performances of the SVM models were superior to those obtained by ERM one. This indicates that SVMcan be used as an alternative modeling tool for QSPR studies.

Key words: Quantitative structure-property relationship, Gas-to-benzene solvation enthalpy, Descriptor, Enhanced replacement method, Support vector machine

Click here to download Supporting Info material in PDF type

Hassan GOLMOHAMMADI,Zahra DASHTBOZORGI,Sajad KHOOSHECHIN. Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds[J]. Acta Phys. -Chim. Sin. 2017, 33(5), 918-926. doi: 10.3866/PKU.WHXB201701163

References

1	Duffy E. M. ; Jorgensen W. L. J. Am. Chem. Soc. 2000, 122, 2878.
2	Cornell W. E. ; Cieplak P. ; Bayly C. I. ; Merz K. M. ; Ferguson D. M. ; Spellmayer D. C. ; Fox T. ; Caldwell J. W. ; Kollman P.A. J. Am. Chem. Soc. 1995, 117, 5179.
3	Graziano G. Can. J. Chem. 2000, 78, 1233.
4	Graziano G. Biophys. Chem. 1999, 82, 69.
5	Graziano G. J. Phys. Chem. B 2000, 104, 9249.
6	Garde S. ; Garcia A. E. ; Pratt L. R. ; Hummer G. Biophys.Chem. 1999, 78, 21.
7	Mintz C. ; Burton K. ; Acree W. E. Jr. ; Abraham M. H. FluidPhase Equilibr. 2007, 258, 191.
8	Chickos, J. S.; Acr
9	ee W. E. Jr. J. Phys. Chem. Ref. Data 2003, 32, 519.
10	Chickos J. S. ; Acree W. E. Jr. J. Phys. Chem. Ref. Data 2002, 31, 537.
11	Borges does Santos R. M. ; Muralha V. S. F. ; Correia C. F. ; Sim?es J. A. M. J. Am. Chem. Soc. 2001, 123, 12670.
12	Laarhoven L. J. J. ; Mulder P. ; Wayner D. D. M. Acc. Chem.Res. 1999, 32, 342.
13	Hansch, C. ; Leo, A. Exploring QSAR: Fundamentals andApplications in Chemistry and Biology, American ChemicalSociety, Washington DC, 1995. doi: 10.1021/jm950902o
14	Bao L. ; Sun Z. R. FEBS Lett. 2002, 521, 109.
15	Belousov A. I. ; Verzakov S. A. ; Von Frese J. Chemom. Intell.Lab. Syst. 2002, 64, 15.
16	Cai Y. D. ; Liu X. J. ; Xu X. B. ; Chou K. C. Comput. Chem. 2002, 26, 293.
17	Morris C. W. ; Autret A. ; Boddy L. Ecol. Model. 2001, 146, 57.
18	Song M. H. ; Breneman C. M. ; Bi J. B. ; Sukumar N. ; Bennett K. P. ; Cramer S. ; Tugcu N. J. Chem. Inf. Comput. Sci. 2002, 42, 1347.
19	Liu H. X. ; Zhang R. S. ; Luan F. ; Yao X. J. ; Liu M. C. ; Hu Z.D. ; Fan B. T. J. Chem. Inf. Comput. Sci. 2003, 43, 900.
20	Liu H. X. ; Zhang R. S. ; Yao X. J. ; Liu M. C. ; Hu Z. D. ; Fan B. T. J. Chem. Inf. Comput. Sci. 2003, 43, 1288.
21	Golmohammadi H. ; Dashtbozorgi Z. ; Acree W. E. Jr. Struct.Chem. 2013, 24, 1799.
22	Golmohammadi H. ; Dashtbozorgi Z. ; Acree W. E. Jr. Phys.Chem. Liq. 2013, 51, 182.
23	Dashtbozorgi Z. ; Golmohammadi H. ; Acree W. E. Jr.Thermochim. Acta 2012, 539, 7.
24	Golmohammadi H. ; Dashtbozorgi Z. ; Acree W. E. Jr. Mol.Inf. 2012, 31, 867.
25	Dashtbozorgi Z. ; Golmohammadi H. ; Acree W. E. Jr. Eur. J.Pharm. Sci. 2012, 47, 421.
26	Mintz C. ; Clark M. ; Burton K. ; Acree W. E. Jr. ; Abraham M.H. QSAR Comb. Sci. 2007, 26, 881.
27	Toubaei A. ; Golmohammadi H. ; Dashtbozorgi Z. ; Acree W.E. Jr. J. Mol. Liq. 2012, 175, 24.
28	Todeschini, R. ; Consonni, V. Molecular Descriptors forChemoinformatics. Wiley VCH: Weinheim, 2009. doi: 10.1002/9783527628766.ch22
29	Hyperchem, re. 4. for Windows, Autodesk, Sansalito, CA, 1995.
30	Todeschini R. ; Consonni V. ; Pavan M. Dragon Software, Milano 2002.
31	Mercader A. G. ; Duchowicz P. R. ; Fernández F. M. ; Castro E.A. J. Chem. Inf. Model. 2011, 51, 1575.
32	MATLAB 7.0, The Mathworks Inc., Natick, MA, USA, 2005, http://www.mathworks.com.
33	Baghban A. ; Ahmadi M. A. ; Pouladi B. ; Amanna B.J. Supercrit. Fluids 2015, 101, 184.
34	Vapnik V. N. ; Lerner A. Autom. Remote Control 1963, 24, 774.
35	Vapnik V. N. ; Chervonenkis A. Y. Autom. Remote Control 1964, 25, 821.
36	Rojas C. ; Duchowicz P. R. ; Tripaldi P. ; Pis Diez R. Chemometr. Intell. Lab. Syst. 2015, 140, 126.
37	Mercader G. ; Duchowicz P. R. ; Fernández F. M. ; Castro E. A. Chemometr. Intell. Lab. Syst. 2008, 92, 138.
38	Gramatica P. QSAR Comb. Sci. 2007, 26, 694.
39	Cao D. S. ; Liang Y. Z. ; Xu Q. S. ; Li H. D. ; Chen X.J. Comput.Chem. 2010, 31, 592.
40	Yan J. ; Huang J. H. ; He M. ; Lu H. B. ; Yang R. ; Kong B. ; Xu Q. S. ; Liang Y. Z. J. Sep. Sci. 2013, 36, 2464.
41	Cao D. S. ; Liang Y. Z. ; Xu Q. S. ; Yun Y. H. ; Li H. D.J. Comput. Aided Mol. Des. 2011, 25, 67.
42	Eriksson L. ; Jaworska J. ; Worth A. P. ; Cronin M. T. ; McDowell R. M. ; Gramatica P. Health Perspect. 2003, 111, 1361.
43	Golbraikh A. ; Shen M. ; Xiao Z. ; Xiao Y. ; Lee K. H. ; Tropsha A. J. Comput. Aided Mol. Des. 2003, 17, 241.
44	Golbraikh A. ; Tropsha A. J. Mol. Graph. Model. 2002, 20, 269.
45	Agrawal V. K. ; Khadikar P.V. Bioorg. Med. Chem. 2001, 911, 3035.
46	Pourbasheer E. ; Riahi S. ; Ganjali M. R. ; Norouzi P.J. Enzyme. Inhib. Med. Chem. 2010, 256, 844.
47	Antipin I. S. ; Arslanov N. A. ; Palyulin V. A. ; Konovalov A. I. ; Zefirov N. S. Dokl. Akad. Nauk. SSSR 1991, 316, 925.
48	Sarkar R. ; Roy A. B. ; Sarkar P. K. Math. Biosci. 1978, 39, 299.
49	Geary R.C. Incorp. Statist. 1954, 5, 15.
50	Moreau G. ; Broto P. Nouv. J. Chim. 1980, 4, 757.
51	Todeschini, R. ; Consonni, V. Handbook of MolecularDescriptors, In: Methods and Principles in MedicinalChemistry; Mannhold, R. , Kubinyi, H. , Timmerman, H. Eds. ; Wiley-VCH: Weinheim, 2000. doi: 10.1002/9783527613106
52	Ma S. ; Lv M. ; Deng F. ; Zhang X. ; Zhai H. ; Lv W. J. Hazard.Mater. 2015, 283, 591.

[1]	Marco FRANCO-PÉREZ,José L. GÁZQUEZ,W. AYERS Paul,Alberto VELA. Thermodynamic Dual Descriptor [J]. Acta Phys. -Chim. Sin., 2018, 34(6): 683-391.
[2]	Chaoxian YAN,Fan YANG,Ruizhi WU,Dagang ZHOU,Xing YANG,Panpan ZHOU. Application of Natural Orbital Fukui Functions and Bonding Reactivity Descriptor in Understanding Bond Formation Mechanisms Underlying [2+4] and [4+2] Cycloadditions of o-Thioquinones with 1, 3-Dienes [J]. Acta Phys. -Chim. Sin., 2018, 34(5): 497-502.
[3]	Xiaoqin DING,Junjie DING,Dayu LI,Li PAN,Chengxin PEI. Toxicity Prediction of Organoph Osphorus Chemical Reactivity Compounds Based on Conceptual DFT [J]. Acta Phys. -Chim. Sin., 2018, 34(3): 314-322.
[4]	Manas GHARA,Pratim K. CHATTARAJ. Bonding and Reactivity in RB-AsR Systems (R=H, F, OH, CH₃, CMe₃, CF₃, SiF₃, BO):Substituent Effects [J]. Acta Phys. -Chim. Sin., 2018, 34(2): 201-207.
[5]	Hassan GOLMOHAMMADI,Zahra DASHTBOZORGI,Sajad KHOOSHECHIN. Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach [J]. Acta Phys. -Chim. Sin., 2017, 33(6): 1160-1170.
[6]	Bing. HE,Yong. LUO,Bing-Ke. LI,Ying. XUE,Luo-Ting. YU,Xiao-Long. QIU,Teng-Kuei. YANG. Predicting and Virtually Screening Breast Cancer Targeting Protein HEC1 Inhibitors by Molecular Descriptors and Machine Learning Methods [J]. Acta Phys. -Chim. Sin., 2015, 31(9): 1795-1802.
[7]	Hai-Chun. LIU,Shuai. LU,Ting. RAN,Yan-Min. ZHANG,Jin-Xing. XU,Xiao. XIONG,An-Yang. XU,Tao. LU,Ya-Dong. CHEN. Accurate Activity Predictions of B-Raf Type II Inhibitors via Molecular Docking and QSAR Methods [J]. Acta Phys. -Chim. Sin., 2015, 31(11): 2191-2206.
[8]	LIU Fen, ZOU Jian-Wei, HU Gui-Xiang, JIANG Yong-Jun. Quantitative Structure-Property Relationship Studies on the Adsorption of Aromatic Contaminants by Carbon Nanotubes [J]. Acta Phys. -Chim. Sin., 2014, 30(9): 1616-1624.
[9]	SHI Jing-Jie, CHEN Li-Ping, CHEN Wang-Hua. QSPR Models of Compound Viscosity Based on Iterative Self-Organizing Data Analysis Technique and Ant Colony Algorithm [J]. Acta Phys. -Chim. Sin., 2014, 30(5): 803-810.
[10]	FU Rong, LU Tian, CHEN Fei-Wu. Comparing Methods for Predicting the Reactive Site of Electrophilic Substitution [J]. Acta Phys. -Chim. Sin., 2014, 30(4): 628-639.
[11]	LI Bing-Ke, CONG Yong, TIAN Zhi-Yue, XUE Ying. Predicting and Virtually Screening the Selective Inhibitors of MMP-13 over MMP-1 by Molecular Descriptors and Machine Learning Methods [J]. Acta Phys. -Chim. Sin., 2014, 30(1): 171-182.
[12]	HAN Na, YUAN Zhe-Ming, CHEN Yuan, DAI Zhi-Jun, WANG Zhi-Ming. Prediction of HLA-A^*0201 Restricted Cytotoxic T Lymphocyte Epitopes Based on High-Dimensional Descriptor Nonlinear Screening [J]. Acta Phys. -Chim. Sin., 2013, 29(09): 1945-1953.
[13]	CONG Yong, XUE Ying. Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods [J]. Acta Phys. -Chim. Sin., 2013, 29(08): 1639-1647.
[14]	WANG Zhi-Ming, HAN Na, YUAN Zhe-Ming, WU Zhao-Hua. Feature Selection for High-Dimensional Data Based on Ridge Regression and SVM and Its Application in Peptide QSAR Modeling [J]. Acta Phys. -Chim. Sin., 2013, 29(03): 498-507.
[15]	LÜ Wei, XUE Ying, MENG Qing-Wei. Classification Prediction of Inhibitors of H1N1 Neuraminidase by Machine Learning Methods [J]. Acta Phys. -Chim. Sin., 2013, 29(01): 217-223.