Multi-Objective Genetic Algorithm (MOGA) As a Feature Selecting Strategy in the Development of Ionic Liquids' Quantitative Toxicity-Toxicity Relationship Models
Quantitative toxicity-toxicity relationship (QTTR) models have a great potential for improving the meaning of toxicological tests conducted on simple organisms. These models allow predicting the toxicological effect of a chemical based on its known toxicological effect in different toxicity tests, e...
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| Veröffentlicht in: | Journal of chemical information and modeling Jg. 58; H. 12; S. 2467 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
24.12.2018
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| ISSN: | 1549-960X, 1549-960X |
| Online-Zugang: | Weitere Angaben |
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| Zusammenfassung: | Quantitative toxicity-toxicity relationship (QTTR) models have a great potential for improving the meaning of toxicological tests conducted on simple organisms. These models allow predicting the toxicological effect of a chemical based on its known toxicological effect in different toxicity tests, even against a different organism. This fact poses a great potential for predicting the toxicity of chemicals against higher organisms based on the results against lower ones. However, the possibility of developing such models is often restricted due to the low availability of data. We present a case study of developing the QTTR model for ionic liquids in different toxicological tests against the same species, in the face of insufficient experimental data (an additional confirmation for a different species is provided in the Supporting Information). In the presented case, we use a series of quantitative structure-activity relationship (QSAR) models developed to deliver the data concerning the toxicity of ionic liquids against human HeLa and MCF-7 cancer cell lines. We use these data to develop a QTTR model with an R
as high as 0.8. The benefit of applying the multi-objective genetic algorithm (MOGA-a genetic algorithm allowing for selection of the best set of explanatory features for several different dependent variables at the same time) as a QSAR model feature selecting strategy is presented and discussed. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1549-960X 1549-960X |
| DOI: | 10.1021/acs.jcim.8b00378 |