A multicriteria decision making approach for ranking gastric cancer drugs based on topological indices and physicochemical properties

Abstract

Chemical graph theory offers a mathematical approach to molecular structure representation and physicochemical property investigation through descriptors based on graph theory. In this study, a QSPR model is developed for analysis of some gastric cancer drug molecules by means of topological indices, regression modeling and multi-criteria decision making (MCDM) approaches. Selected topological indices were derived from the molecular graphs and used as independent variables for modeling of selected physicochemical properties by means of inverse and cubic regression models. Statistical comparison of the two types of models showed that the cubic models exhibited a goodness-of-fit than inverse models for the investigated properties in the current dataset. Additionally, selected topological indices were used as criteria in TOPSIS and SAW to obtain a mathematical ranking without taking into account any other information but the selected structural descriptors. Such rankings refer to computational comparisons based on the selected graph theoretical criteria, and do not reflect drug effectiveness, clinical applicability, superiority and recommended treatments. The study shows how chemical graph theory, QSPR analysis, and MCDM methods have been used for computation and description-based comparison of structures of molecules.

Abstract Chemical graph theory offers a mathematical approach to molecular structure representation and physicochemical property investigation through descriptors based on graph theory. In this study, a QSPR model is developed for analysis of some gastric cancer drug molecules by means of topological indices, regression modeling and multi-criteria decision making (MCDM) approaches. Selected topological indices were derived from the molecular graphs and used as independent variables for modeling of selected physicochemical properties by means of inverse and cubic regression models. Statistical comparison of the two types of models showed that the cubic models exhibited a goodness-of-fit than inverse models for the investigated properties in the current dataset. Additionally, selected topological indices were used as criteria in TOPSIS and SAW to obtain a mathematical ranking without taking into account any other information but the selected structural descriptors. Such rankings refer to computational comparisons based on the selected graph theoretical criteria, and do not reflect drug effectiveness, clinical applicability, superiority and recommended treatments. The study shows how chemical graph theory, QSPR analysis, and MCDM methods have been used for computation and description-based comparison of structures of molecules.