Prediction of ionic liquids toxicity using machine learning models for application to gas hydrate
Process Safety Progress, ISSN: 1547-5913, Vol: 43, Issue: S1, Page: S199-S212
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
Ionic liquids (ILs) are highly favored in the oil and gas industry as gas hydrate inhibitors due to their dual functionality as thermodynamic inhibitor and kinetic hydrate inhibitor. Though known as the “green alternatives,” concerns about the effects of ILs in the environment are rising such that ILs can stabilize in water systems. Furthermore, there are insufficient data on the toxicity of ILs, limiting the use of ILs for industrial applications. Ridge, LASSO, decision tree, random forest, extra tree, gradient boost, and support vector regressions were used to develop IL toxicity predictive models. Random forest yielded the strongest predictive performance, scoring the highest R value of 0.86, with mean absolute error and root mean square error values of 0.32 and 0.43, respectively. Feature selections were conducted to investigate the contributions of the five molecular descriptors involved in developing regression models in this work. Descriptor MSD was found to contribute the highest at 67% in predicting the toxicity of ILs, followed by SNar and MAXDP, demonstrating contributions of 15.2% and 14.1%, respectively. Further quantitative structure–activity relationship model validations were executed; the use of three descriptors resulted in a 2% increase in predictive performance for decision tree regression, whereas R values remained the same for random forest, extra tree, and gradient boosting.
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