Exploring Predictive QSAR Models Using Quantum Topological Molecular Similarity (QTMS) Descriptors for Toxicity of Nitroaromatics to Saccharomyces cerevisiae

Abstract

AbstractIn view of the widespread industrial use of nitroaromatics and their consequent ecotoxicological hazard potential, we constructed predictive Quantitative Structure–Activity Relationship (QSAR) models for the toxicity of nitroaromatics to the ecologically important species Saccharomyces cerevisiae. We used Quantum Topological Molecular Similarity (QTMS) descriptors along with electrophilicity index (ELUMO) and lipid water partition coefficient (log Kow) as predictor variables. The QTMS descriptors were calculated at B3LYP/6‐311+G(2d,p) level of theory. QTMS descriptors were employed to complement the deficiency of ELUMO in setting up predictive QSAR models from the view point of external validation. The dataset was divided into a training set (18 compounds) and test set (six compounds) in a ratio of three to one. Partial Least Square (PLS) models were developed based on the training set compounds. The predictive capacity of the models was assessed by the test compounds. The models were also validated by a randomisation test and leave‐one‐seventh‐out crossvalidation test. The results suggest that Bond Critical Point (BCP) descriptors can develop predictive QSAR models for nitroaromatic toxicity to Saccharomyces cerevisiae when used along with ELUMO and log Kow. The diagnostic potential of QTMS descriptors could also reveal the importance of the nitro group for nitroaromatic toxicity.