Use of a neural network to determine the boiling point of alkanes

Back-propagation neural networks (NNs) are useful for the study of quantitative structure–activity relationships or structure–property correlations. Models of relationships between structure and boiling point (bp) of 150 alkanes were constructed by means of a multilayer neural network (NN) using the back-propagation algorithm. The results of our NN were compared with those of other models from the literature, and found to be better. The boiling points of the 150 alkanes were then predicted by removing 15 compounds (test set) and using the 135 other molecules as a training set. Using the same process, all the compounds in the data bank were then predicted in groups of 15 compounds. The results obtained were satisfying.

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

[2]  M. Randic.  Characterization of molecular branching , 1975 .

[3]  Nenad Trinajstić,et al.  Computer enumeration and generation of trees and rooted trees , 1981, J. Chem. Inf. Comput. Sci..

[4]  Paul G. Seybold,et al.  Molecular structure: Property relationships , 1987 .

[5]  P. Seybold,et al.  Molecular modeling of the physical properties of the alkanes , 1988 .

[6]  T. Sejnowski,et al.  Predicting the secondary structure of globular proteins using neural network models. , 1988, Journal of molecular biology.

[7]  J. U. Thomsen,et al.  Pattern recognition of the 1H NMR spectra of sugar alditols using a neural network , 1989 .

[8]  H Ichikawa,et al.  Neural networks applied to quantitative structure-activity relationship analysis. , 1990, Journal of medicinal chemistry.

[9]  Gerald M. Maggiora,et al.  Applications of neural networks in chemistry. 1. Prediction of electrophilic aromatic substitution reactions , 1990, J. Chem. Inf. Comput. Sci..

[10]  H Ichikawa,et al.  Neural networks applied to structure-activity relationships. , 1990, Journal of medicinal chemistry.

[11]  Tomoo Aoyama,et al.  Basic Operating Characteristics of Neural Networks When Applied to Structure-Activity Studies , 1991 .

[12]  T. A. Andrea,et al.  Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. , 1991, Journal of medicinal chemistry.

[13]  Tomoo Aoyama,et al.  Obtaining the correlation indices between drug activity and structural parameters using a neural network , 1991 .

[14]  Nicholas Bodor,et al.  Neural network studies. 1. Estimation of the aqueous solubility of organic compounds , 1991 .

[15]  S. So,et al.  Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors. , 1992, Journal of medicinal chemistry.

[16]  Sonja Nikolic,et al.  Comparative study of molecular descriptors derived from the distance matrix , 1992, J. Chem. Inf. Comput. Sci..

[17]  Zlatko Mihalić,et al.  A graph-theoretical approach to structure-property relationships , 1992 .