QSTR with Extended Topochemical Atom (ETA) Indices 8.^a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

Abstract

Quantitative structure-toxicity relationship (QSTR) study has proved to be a valuable approach in ecotoxicity estimations of acute and chronic toxicity to various organisms, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. In this background, we have modeled the inhibition of 41 substituted phenols on germination rate of Cucumis sativus with extended topochemical atom (ETA) indices using different chemometric tools and compared the ETA relations with non-ETA models derived from different topological indices (Wiener W, Balaban J, Hosoya Z, Zagreb, molecular connectivity indices, E-state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef, H_bond_acceptor, H_bond_donor, MW, logK ow and E lumo ). Different statistical tools used in this communication are stepwise regression analysis, multiple linear regression with factor analysis as the preprocessing step for variable selection (FA-MLR), multiple linear regression with genetic function approximation (GFA-MLR), partial least squares regression with factor analysis as the preprocessing step for variable selection (FA-PLS), genetic partial least squares (G/PLS) and principal component regression analysis (PCRA). The results show that the best statistical quality along with prediction statistics with ETA descriptors is found in case of GFA-MLR equation. In case of non-ETA descriptors, the best equation is obtained from PCRA considering both equation statistics and predictive ability. When combined descriptors were considered, FA-PLS gave the best equation. The best ETA equation (GFA-MLR) has better leave-one-out Q 2 value than the best non-ETA equation (PCRA). The results suggest that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing significantly to the comparative inhibition activity of substituted phenols on germination rate of Cucumis sativus.

QSPR of n‐Octanol/Water Partition Coefficient of Nonionic Organic Compounds Using Extended Topochemical Atom (ETA) Indices

“…The objective of FA is to reduce the number of variables of possible importance in characterizing an array of numbers. The details of FA-MLR have been described in previous communications [26,27].…”

“…The basic procedures involve (1) identifying an initial model, (2) iteratively "stepping", that is, repeatedly altering the model at the previous step by adding or removing a predictor variable in accordance with the "stepping criteria" (partial F-statistic; F ¼ 4 for in- clusion; F ¼ 3.5 for exclusion) and (3) terminating the search when stepping is no longer possible given the stepping criteria, or when a specified maximum number of steps has been reached [26,27].…”

“…Initially, variables for PLS were selected based on the factor loading tables and PLS regression was repeated, each time eliminating the variable with the minimum standardized regression coefficient value, until the model giving the maximum Q 2 value was obtained [26,27]. The stepwise regression and PLS analyses were performed using MINITAB software [40].…”

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

“…The ETA [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] indices formulated based on modification of TAU descriptors [38][39][40][41][42][43][44][45][46][47][48][49][50], which were developed in the valence electron mobile (VEM) environment, have been used to develop the QSTR models for 40-h T. pyriformis toxicity of 77 aromatic aldehydes. Some basic parameters of the ETA scheme used in the development of QSTR models for the above mentioned dataset have been defined in Supplementary Materials section.…”

QSPR of the bioconcentration factors of non-ionic organic compounds in fish using extended topochemical atom (ETA) indices

“…In the present communication, we have modelled the log BCF data of 122 non-ionic organic compounds in fish [31] using the recently introduced extended topochemical atom (ETA) indices [32][33][34][35][36][37][38][39], which were developed in the valence electron mobile (VEM) environment as an extension of the TAU concept [40][41][42][43][44][45][46][47][48][49][50][51][52]. For this purpose, the multiple linear regression (MLR) technique, stepwise regression analysis, partial least squares (PLS) analysis and principal component regression analysis (PCRA) were used as the statistical tools.…”