Quantitative Structure-Property Relationships for the Prediction of Vapor Pressures of Organic Compounds from Molecular Structures

A quantitative structure-property relationship (QSPR) is developed to relate the molecular structures of 420 diverse organic compounds to their vapor pressures at 25 degrees C expressed as log(vp), where vp is in pascals. The log(vp) values range over 8 orders of magnitude from -1.34 to 6.68 log units. The compounds are encoded with topological, electronic, geometrical, and hybrid descriptors. Statistical and computational neural network (CNN) models are built using subsets of the descriptors chosen by simulated annealing and genetic algorithm feature selection routines. An 8-descriptor CNN model, which contains only topological descriptors, is presented which has a root-mean-square (rms) error of 0.37 log unit for a 65-member external prediction set. A 10-descriptor CNN model containing a larger selection of descriptor types gives an improved rms error of 0.33 log unit for the external prediction set.

[1]  Peter C. Jurs,et al.  Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing , 1995, J. Chem. Inf. Comput. Sci..

[2]  Peter C. Jurs,et al.  Prediction of boiling points and critical temperatures of industrially important organic compounds from molecular structure , 1994, J. Chem. Inf. Comput. Sci..

[3]  David T. Stanton,et al.  Computer-assisted prediction of normal boiling points of furans, tetrahydrofurans, and thiophenes , 1991, J. Chem. Inf. Comput. Sci..

[4]  Peter C. Jurs,et al.  Prediction of Normal Boiling Points for a Diverse Set of Industrially Important Organic Compounds from Molecular Structure , 1995, J. Chem. Inf. Comput. Sci..

[5]  Cikui Liang,et al.  QSPR Prediction of Vapor Pressure from Solely Theoretically-Derived Descriptors , 1998, J. Chem. Inf. Comput. Sci..

[6]  P. Jurs,et al.  Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies , 1990 .

[7]  L. Kier.  Distinguishing Atom Differences in a Molecular Graph Shape Index , 1986 .

[8]  L B Kier,et al.  Molecular connectivity VII: specific treatment of heteroatoms. , 1976, Journal of pharmaceutical sciences.

[9]  Subhash C. Basak,et al.  Use of Topostructural, Topochemical, and Geometric Parameters in the Prediction of Vapor Pressure: A Hierarchical QSAR Approach , 1997, J. Chem. Inf. Comput. Sci..

[10]  D. Manallack,et al.  Statistics using neural networks: chance effects. , 1993, Journal of medicinal chemistry.

[11]  Peter C. Jurs,et al.  Prediction of Vapor Pressures of Hydrocarbons and Halohydrocarbons from Molecular Structure with a Computational Neural Network Model , 1999, J. Chem. Inf. Comput. Sci..

[12]  L. Burkhard,et al.  Estimation of vapor pressures for polychlorinated biphenyls: a comparison of eleven predictive methods. , 1985, Environmental science & technology.

[13]  Terry R. Stouch,et al.  A simple method for the representation, quantification, and comparison of the volumes and shapes of chemical compounds , 1986, J. Chem. Inf. Comput. Sci..

[14]  Milan Randic,et al.  Search for all self-avoiding paths graphs for molecular graphs , 1979, Comput. Chem..

[15]  D. B. Hibbert.  Genetic algorithms in chemistry , 1993 .

[16]  A. Site,et al.  The Vapor Pressure of Environmentally Significant Organic Chemicals: A Review of Methods and Data at Ambient Temperature , 1997 .

[17]  H. Wiener.  Structural determination of paraffin boiling points. , 1947, Journal of the American Chemical Society.

[18]  G. R. Famini,et al.  Using theoretical descriptors in structure–activity relationships: Solubility in supercritical CO2 , 1993 .

[19]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 1. Concepts, properties and context , 1993 .

[20]  Yilin Wang,et al.  QSPR Studies on Vapor Pressure, Aqueous Solubility, and the Prediction of Water-Air Partition Coefficients , 1998, J. Chem. Inf. Comput. Sci..

[21]  James J. P. Stewart,et al.  MOPAC: A semiempirical molecular orbital program , 1990, J. Comput. Aided Mol. Des..

[22]  Matthew D. Wessel,et al.  Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks , 1994 .

[23]  Jon W. Ball,et al.  Quantitative structure‐activity relationships for toxicity of phenols using regression analysis and computational neural networks , 1994 .

[24]  Milan Randic,et al.  On molecular identification numbers , 1984, J. Chem. Inf. Comput. Sci..

[25]  J. Topliss,et al.  Chance factors in studies of quantitative structure-activity relationships. , 1979, Journal of medicinal chemistry.

[26]  L B Kier,et al.  Molecular connectivity. I: Relationship to nonspecific local anesthesia. , 1975, Journal of pharmaceutical sciences.