Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties
GRACE Amabel Tabaaza, BENNET Nii Tackie-Otoo, DZULKARNAIN B Zaini, BHAJAN Lal
download PDFAbstract. Owing to the rapid growth in IL synthesis due to feasible cation–anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico principal properties (PPs) as descriptors. Deploying this properties aid in considering both the effect of cations and anions on Vibrio fischeri toxicity prediction. Among the models trained, the Random Forest model proved to be the most precise nevertheless, decision tree model was the most accurate and consistent. Considering the importance of the descriptors to Vibrio fischeri toxicity selection techniques and model optimization.
Keywords
Ionic liquids, Vibrio Fischeri, Toxicity, Machine learning, QSPR/QSAR and Principal Properties
Published online 5/20/2023, 14 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: GRACE Amabel Tabaaza, BENNET Nii Tackie-Otoo, DZULKARNAIN B Zaini, BHAJAN Lal, Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties, Materials Research Proceedings, Vol. 29, pp 234-247, 2023
DOI: https://doi.org/10.21741/9781644902516-27
The article was published as article 27 of the book Sustainable Processes and Clean Energy Transition
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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