[1P3] [General Session] 3. Data Mining

[1P3-04] Studies on Normal Boiling Point of Chemical Substances and Eigen Values of Their Molecular Matrices

〇Kiryu Yamashita¹, Yoshimasa Takahashi¹, Tetsuo Katsuragi¹ (1. Graduate School of Engineering , Toyohashi University of Technology)

Keywords:Molecular Structure, Graph Mining, Prediction of Molecular Properties

Boiling point is one of the most basic physical properties of chemical compounds. If it is possible to know the boiling point, it can be used to identify an unknown compound. The boiling point can be also used to predict other physical properties of the compound. Thus, estimation of boiling point is one of the important research areas in computational chemistry, and is still being studied actively. It is empirically known that there is a close relationship between chemical structure and physical properties, so that physical properties can be thought of as a function of chemical structure. On other hand, various graph invariants derived from molecular graphs can also be considered as a function of chemical structure. In this study, we tried to construct a prediction model by multiple linear regression analysis using eigenvalues obtained from matrix representations of molecular graphs. For 51 chemical compounds of saturated hydrocarbons, a regression model obtained with the first four largest eigenvalues and the number of carbon atoms gave us good estimates of their boiling points.