QSTR with Extended Topochemical Atom Indices. 4. Modeling of the Acute Toxicity of Phenylsulfonyl Carboxylates to Vibrio fischeri using Principal Component Factor Analysis and Principal Component Regression Analysis

Abstract

AbstractThe present paper deals with modeling of the acute toxicity of 56 phenylsulfonyl carboxylates to Vibrio fischeri. Principal component factor analysis has been used as the data‐preprocessing step for the selection of independent variables for the subsequent multiple regression analysis. The statistical quality of the best model using ETA descriptors is as follows: n=56, Q2=0.726, Ra2=0.837, R=0.923, F=57.4 (df 5, 50), s=0.186, AVRES=0.136. Attempt has also been made to model the data set with different non‐ETA parameters (topological indices including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E‐State parameters apart from physicochemical parameters like AlogP98, MolRef and H‐bond‐acceptor) and the best model shows the following quality: n=56, Q2=0.763, Ra2=0.798, R=0.903, F=44.3 (df 5, 50), s=0.207, AVRES=0.139. An attempt to use both ETA and non‐ETA parameters has lead to an equation which is somewhat better in statistical quality than the best ETA model: n=56, Q2=0.779, Ra2=0.861, R=0.935, F=69.4 (df 5, 50), s=0.172, AVRES=0.127. Use of the ETA indices has suggested that negative contributions of steric bulk, branching, functionality of C10, functionality of chloro substituent at X1 position and presence of unsaturation at the substituent(s) on C10, and positive contributions of functionality of O13 and presence of substituents with electronegative atoms at R2 and R3 positions. Using factor scores as independent variables, principal component regression analysis has been performed and the derived relations are of excellent statistical qualities (Q2 values being 0.816, 0.828 and 0.848 while R2 values being 0.894, 0.871 and 0.905 for factor scores derived from ETA, non‐ETA and combined matrices respectively) which are comparable to that (Q2 and R2 values being 0.790 and 0.920 respectively) of the relation generated from comparative molecular field analysis on the same data set.