Predicting the vapour pressure of chemicals from structure: A comparison of graph theoretic versus quantum chemical descriptors

SAR and QSAR in Environmental Research

Volumn 20, Issue 1-2, 2009, Pages 119-132

  Basak, S C ^a   Mills, D ^a  

^a UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

Predictive model; Quantitative structure property relationship (QSPR); Quantum chemical descriptor; Ridge regression; Topological descriptor; Vapour pressure

Indexed keywords

DENSITY FUNCTIONAL THEORY; GRAPH THEORY; HYDROSTATIC PRESSURE; INDICATORS (CHEMICAL); INDUSTRIAL CHEMICALS; METALLORGANIC CHEMICAL VAPOR DEPOSITION; QUANTUM CHEMISTRY; REGRESSION ANALYSIS; VAPOR PRESSURE;

CHLORINATED ORGANICS; GRAPH THEORETICS; MOLECULAR DESCRIPTORS; PREDICTIVE MODELS; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP; QUANTUM CHEMICAL DESCRIPTORS; RIDGE REGRESSION; TOPOLOGICAL DESCRIPTORS; VAPOUR PRESSURES;

ORGANIC CHEMICALS;

CHLORINATED HYDROCARBON;

ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; THEORETICAL MODEL; VAPOR PRESSURE;

HYDROCARBONS, CHLORINATED; MODELS, THEORETICAL; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; VAPOR PRESSURE;

EID: 67650914244     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/10629360902726007     Document Type: Article

References (40)

1. H. Engelhardt, H.E. McClelland, and P.C. Jurs, Quantitative structure-property relationships for the prediction of vapor pressures of organic compounds from molecular structures, J. Chem. Inf. Comput. Sci. 40 (2000), pp. 967-975.
2. A.R. Katritzky, Y. Wang, S. Sild, and T. Tamm, QSPR studies on vapor pressure, aqueous solubility, and the prediction of water-air partition coefficients, J. Chem. Inf. Comput. Sci. 38 (1998), pp. 720-725.
3. S.C. Basak and D. Mills, Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors, ARKIVOC 2005 (2005), pp. 308-320.
4. C. Liang and D.A. Gallagher, QSPR prediction of vapor pressure from solely theoreticallyderived descriptors, J. Chem. Inf. Comput. Sci. 38 (1998), pp. 321-324.
5. M. Staikova, F. Wania, and D.J. 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.
6. S.C. Basak, D.K. Harriss, and V.R. Magnuson, POLLY v. 2.3, University of Minnesota, 1988.
7. P.A. Filip, T.S. Balaban, and A.T. Balaban, A new approach for devising local graph invariants: Derived topological indices with low degeneracy and good correlational ability, J. Math. Chem. 1 (1987), pp. 61-83.
8. Molconn-Z Version 3.5, Hall Associates Consulting, Quincy, MA, 2000; software available at http://www.edusoft-lc.com/molconn.
9. M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, J.A. Montgomery, T. Vreven, K.N. Kudin, J.C. Burant, J.M. Millam, S.S. Iyengar, J. Tomasi, V. Barone, B. Mennucci, M. Cossi, G. Scalmani, N. Rega, G.A. Petersson, M. Nakatsuji, M. Hada, M. Ehara, K. Toyota, J. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, M. Klene, X. Li, J.E. Knox, H.P. Hratchian, J.B. Cross, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J. Ochterski, P.Y. Ayala, V.G. Zakrzewski, S. Dapprich, A.D. Daniels, M.C. Strain, O. Farkas, D.K. Malick, A.D. Rabuck, K. Raghavachari, J.B. Foresman, J.V. Ortiz, Q. Cui, A.G. Baboul, S. Clifford, J. Cioslowski, B.B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R.L. Martin, D.J. Fox, T. Keith, M.A. Al-Laham, C.Y. Peng, A. Nanayakkara, M. Challacombe, P.M.W. Gill, B. Johnson, W. Chen, M.W. Wong, C. Gonzalez, and J.A. Pople, Gaussian 03W, Version 6.0 (Revision C.02), 2004; software available at http://www.gaussian.com/ g03w-req.htm.
10. L.B. Kier, W.J. Murray, M. Randic, and L.H. Hall, Molecular connectivity. V. Connectivity series concept applied to diversity, J. Pharm. Sci. 65 (1976), pp. 1226-1230.
11. M. Randic, On characterization of molecular branching, J. Am. Chem. Soc. 97 (1975), pp. 6609-6615.
12. L.B. Kier and L.H. Hall, Molecular Connectivity in Structure-Activity Analysis, Research Studies Press, Letchworth, 1986.
13. A.T. Balaban, Highly discriminating distance-based topological indices, Chem. Phys. Lett. 89 (1982), pp. 399-404.
14. S.C. Basak, Information theoretic indices of neighborhood complexity and their applications, in Topological Indices and Related Descriptors in QSAR and QSPR, J. Devillers and A.T. Balaban, eds., Gordon and Breach, Amsterdam, 1999, pp. 563-593.
15. S.C. Basak, Use of molecular complexity indices in predictive pharmacology and toxicology: A QSAR approach, Med. Sci. Res. 15 (1987), pp. 605-609.
16. L.B. Kier and L.H. Hall, An electrotopological-state index for atoms in molecules, Pharm. Res. 8 (1990), pp. 801-807.
17. L.B. Kier and L.H. Hall, Molecular Structure Description: The Electrotopological State, Academic Press, San Diego, CA, 1999.
18. SAS Institute, Inc, In SAS/STAT User Guide, Release 6.03 Edition, SAS Institute Inc., Cary, NC, 1988.
19. A.E. Hoerl and R.W. Kennard, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics 12 (1970), pp. 55-67.
20. A.E. Hoerl and R.W. Kennard, Ridge regression: Applications to nonorthogonal problems, Technometrics 12 (2005), pp. 69-82.
21. I.E. Frank and J.H. Friedman, A statistical view of some chemometrics regression tools, Technometrics 35 (1993), pp. 109-135.
22. S. Wold, Discussion: PLS in chemical practice, Technometrics 35 (1993), pp. 136-139.
23. D. Hawkins, S. Basak, and X. Shi, QSAR with few compounds and many features, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 663-670.
24. A. Hoskuldsson, A combined theory for PCA and PLS, J. Chemom. 2 (1988), pp. 211-228.
25. A. Hoskuldsson, A combined theory for PCA and PLS, J. Chemom. 9 (1995), pp. 91-123.
26. S.C. Basak, D. Mills, M.M. Mumtaz, and K. Balasubramanian, Use of topological indices in predicting aryl hydrocarbon receptor binding potency of dibenzofurans: A hierarchical QSAR approach, Indian J. Chem. 42A (2003), pp. 1385-1391.
27. S.C. Basak, D. Mills, D.M. Hawkins, and H.A. El-Masri, Prediction of tissue: Air partition coefficients: A comparison of structure-based and property-based methods, SAR QSAR Environ. Res. 13 (2002), pp. 649-665.
28. S.C. Basak, D. Mills, and B.D. Gute, Prediction of tissue: Air partition coefficients - theoretical vs experimental methods, SAR QSAR Environ. Res. 17 (2006), pp. 515-532.
29. S.C. Basak, D. Mills, D.M. Hawkins, and H. El-Masri, Prediction of human blood: Air partition coefficient: A comparison of structure-based and property-based methods, Risk Anal. 23 (2003), pp. 1173-1184.
30. S.C. Basak, D. Mills, H.A. El-Masri, M.M. Mumtaz, and D.M. Hawkins, Predicting blood: Air partition coefficients using theoretical molecular descriptors, Environ. Toxicol. Pharmacol. 16 (2004), pp. 45-55.
31. D.M. Hawkins, LinMods v. 5.0, School of Statistics, University of Minnesota, Minneapolis, MN, 2005.
32. D.M. Hawkins, S.C. Basak, and D. Mills, Assessing model fit by cross-validation, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 579-586.
33. H.A. Martens, Determining rank and evaluating performance in regression: Independent test sets or cross-validation/cross-evaluation? in 19th Symposium on Applied Statistics, L. Norgaard and A. Hoskuldsson, eds., 1997, pp. 14-44.
34. D.M. Hawkins, J.J. Kraker, S.C. Basak, andD. Mills, QSAR checking and validation: A case study with hydroxy radical reaction rate constant, SAR QSAR Environ. Res., 19 (2008), pp. 529-539.
35. J.J. Kraker, D.M. Hawkins, S.C. Basak, R. Natarajan, and D. Mills, Quantitative structure-activity relationship (QSAR) modeling of juvenile hormone activity: Comparison of validation procedures, Chemom. Intell. Lab. Syst. 87 (2007), pp. 33-42.
36. S.C. Basak, R. Natarajan, D. Mills, D.M. Hawkins, and J.J. Kraker, Quantitative structureactivity relationship modeling of juvenile hormone mimetic compounds for Culex pipiens Larvaewith discussion of descriptor thinning methods, J. Chem. Inf. Model. 46 (2006), pp. 65-77.
37. B.D. Gute and S.C. Basak, Predicting acute toxicity of benzene derivatives using theoretical molecular descriptors: A hierarchical QSAR approach, SAR QSAR Environ. Res. 7 (1997), pp. 117-131.
38. B.D. Gute, G.D. Grunwald, and S.C. Basak, Prediction of the dermal penetration of polycyclic aromatic hydrocarbons (PAHs): A hierarchical QSAR approach, SAR QSAR Environ. Res. 10 (1999), pp. 1-15.
39. S.C. Basak, D.R. Mills, A.T. Balaban, and B.D. Gute, Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: A hierarchical QSAR approach, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 671-678.
40. S.C. Basak, R. Natarajan, and D. Mills, Structure-activity relationships for mosquito repellent aminoamides using the hierarchical QSAR method based on calculated molecular descriptors, in Conference Proceedings: WSEAS Transactions on Information Science and Applications, 2005, p. 958