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Development of Predictive Linear and Non-linear QSTR Models for Aliivibrio Fischeri Toxicity of Deep Eutectic Solvents

Development of Predictive Linear and Non-linear QSTR Models for Aliivibrio Fischeri Toxicity of Deep Eutectic Solvents

Amit Kumar Halder, M. Natália D. S. Cordeiro
Copyright: © 2019 |Volume: 4 |Issue: 4 |Pages: 20
ISSN: 2379-7487|EISSN: 2379-7479|EISBN13: 9781522570554|DOI: 10.4018/IJQSPR.2019100104
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MLA

Halder, Amit Kumar, and M. Natália D. S. Cordeiro. "Development of Predictive Linear and Non-linear QSTR Models for Aliivibrio Fischeri Toxicity of Deep Eutectic Solvents." IJQSPR vol.4, no.4 2019: pp.50-69. http://doi.org/10.4018/IJQSPR.2019100104

APA

Halder, A. K. & Cordeiro, M. N. (2019). Development of Predictive Linear and Non-linear QSTR Models for Aliivibrio Fischeri Toxicity of Deep Eutectic Solvents. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 4(4), 50-69. http://doi.org/10.4018/IJQSPR.2019100104

Chicago

Halder, Amit Kumar, and M. Natália D. S. Cordeiro. "Development of Predictive Linear and Non-linear QSTR Models for Aliivibrio Fischeri Toxicity of Deep Eutectic Solvents," International Journal of Quantitative Structure-Property Relationships (IJQSPR) 4, no.4: 50-69. http://doi.org/10.4018/IJQSPR.2019100104

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Abstract

Deep eutectic solvents (DESs) have emerged as a very important group of chemicals in recent years. Although generally considered as environment friendly or ‘green,' recent investigations reported toxic behaviors of some DESs towards various biological species. In this work, quantitative structure toxicity relationship analysis was performed on a dataset containing 72 DESs and their components to find the structural determinants responsible for higher DES mediated toxicity. Additionally, efficiencies of various machine learning tools as well as different feature selection algorithms were estimated. To understand the true predictivity of the derived models, three external validation strategies, namely ‘points out,' ‘mixtures out,' and ‘compounds out' were applied along with an ‘all out' technique, a modification of the earlier reported ‘everything out' validation. The models highlight importance of the number of nitrogen atoms, the van der Waals surface area, molar refractivity, dipole moment and molecular mass for shaping the toxicity of DESs and their components towards A. fischeri.

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