QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes toTetrahymena pyriformis

Abstract

Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure-activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K(o/w)), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K(o/w).

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

“…The comparison shows higher predictive ability of the models presently developed by us than those reported previously as evidenced from the values of internal and external validation parameters. It is interesting to note that quality of some of the models developed here using only topological parameters along with log K o/w are better in statistical quality than those reported previously [22] and developed using, along with log K o/w , quantum chemical descriptors calculated at sufficiently high level of theory. Topological descriptors may be computed in a straight-forward approach making them less computer intensive than quantum chemical descriptors.…”

“…Their best model for 77 compounds showed a model R 2 value of 0.805 and corresponding cross-validated R 2 of 0.789 [4]. Kar et al [22] developed QSAR models on the same dataset using QTMS descriptors along with log K o/w and reported the quality of their model in terms of internal as well as external validation parameters. The best model in that work showed a R 2 value of 0.829, leave-one-out cross validated R 2 (Q 2 (LOO) ) value of 0.804 (internal validation parameter), and predictive R 2 (R 2 pred ) value of 0.886 (external validation parameter) [22].…”

“…Kar et al [22] developed QSAR models on the same dataset using QTMS descriptors along with log K o/w and reported the quality of their model in terms of internal as well as external validation parameters. The best model in that work showed a R 2 value of 0.829, leave-one-out cross validated R 2 (Q 2 (LOO) ) value of 0.804 (internal validation parameter), and predictive R 2 (R 2 pred ) value of 0.886 (external validation parameter) [22]. In our present work, we have reported both internal and external validation parameters along Table 5 Comparison of statistical parameters of the models developed in the present work with previously reported models.…”

“…Kar et al developed QSAR models on the same dataset using quantum topological molecular similarity (QTMS) descriptors along with log K o/w and obtained encouraging results [22].…”

SVEEVA Descriptor Application to Peptide QSAR

“…In this paper, based on quantum chemistry calculation level of density function theory (DFT) [19,20], Berny energetic gradient and generalized gradient approximation (GGA) were employed to optimize the spatial conformation of coded amino acids. Then, the descriptor calculation web of molecular descriptor lab (MODEL) [21] …”