Comparative performance of descriptors in a multiple linear and Kriging models: a case study on the acute toxicity of organic chemicals to algae

Abstract

This study presents quantitative structure-toxicity relationship (QSTR) models on the toxicity of 91 organic compounds to Chlorella vulgaris using multiple linear regression (MLR) and Kriging techniques. The molecular descriptors were calculated using SPARTAN and DRAGON programs, and descriptor selection was made by "all subset" method available in the QSARINS software. MLR and Kriging models developed with the same descriptors were compared. In addition to these models, Kriging method was used for descriptor selection, and model development. The selected descriptors showed the importance of hydrophobicity, molecular weight and atomic ionization state in describing the toxicity of a diverse set of chemicals to C. vulgaris. A QSTR model should be associated with appropriate measures of goodness-of-fit, robustness, and predictivity in order to be used for regulatory purpose. Therefore, while the internal performances (goodness-of-fit and robustness) of the models were determined by using a training set, the predictive abilities of the models were determined by using a test set. The results of the study showed that while MLR method is easier to apply, the Kriging method was more successful in predicting toxicity.

Project performance prediction model linking agility and flexibility demands to project type

“…Examples are the works conducted by Bárdossy and Pegram (2014), and Brus and Heuvelink (2007) showing that the Kriging method outperforms MLR producing small absolute errors in hydrology and environmental studies. Tugcu et al (2014) found similar results comparing both methods (Kriging and MLR) in terms of toxicity predictions of organic compounds. Sihag et al (2017) proposed the use of the Gaussian process (GP) method for modeling the infiltration of sandy soil and found better results than with MLR application.…”

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

“…Computational methods have been developed over many years and play a critical role in estimating the aquatic toxicity of both new and existing compounds [ 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Quantitative structure–activity relationships (QSARs) are an effective method for predicting the aquatic toxicity of chemicals [ 40 , 41 , 42 , 43 , 44 , 45 ].…”