Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models

Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models

Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase...

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Main Authors: Peng Zhu, Xuejing Kang, Yongsheng Zhao, Ullah Latif, Hongzhong Zhang
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:International Journal of Molecular Sciences
Subjects:
toxicity
ionic liquids
acetylcholinesterase enzyme
extreme learning machine
multiple linear regression
Online Access:https://www.mdpi.com/1422-0067/20/9/2186
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spelling doaj-8b2420f2d0bb4ca29e1c7fbf4b4a81522020-11-25T01:38:41ZengMDPI AGInternational Journal of Molecular Sciences1422-00672019-05-01209218610.3390/ijms20092186ijms20092186Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR ModelsPeng Zhu0Xuejing Kang1Yongsheng Zhao2Ullah Latif3Hongzhong Zhang4School of Materials Science and Energy Engineering, Foshan University, Foshan 528000, ChinaSchool of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaDepartment of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, USADepartment Beijing Key Laboratory of Ionic Liquids Clean Process, Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaSchool of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaLimited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using multiple linear regression (MLR) and extreme learning machine (ELM) algorithms. The structures of 57 cations and 21 anions were optimized using quantum chemistry calculations. The electrostatic potential surface area (<i>S</i><sub>EP</sub>) and the screening charge density distribution area (<i>S</i><sub>&#963;</sub>) descriptors were calculated and used for prediction of IL toxicity. Performance and predictive aptitude between MLR and ELM models were analyzed. Highest squared correlation coefficient (<i>R</i><sup>2</sup>), and also lowest average absolute relative deviation (AARD%) and root-mean-square error (RMSE) were observed for training set, test set, and total set for the ELM model. These findings validated the superior performance of ELM over the MLR toxicity prediction model.https://www.mdpi.com/1422-0067/20/9/2186toxicityionic liquidsacetylcholinesterase enzymeextreme learning machinemultiple linear regression
collection DOAJ
language English
format Article
sources DOAJ
author Peng Zhu
Xuejing Kang
Yongsheng Zhao
Ullah Latif
Hongzhong Zhang
spellingShingle Peng Zhu
Xuejing Kang
Yongsheng Zhao
Ullah Latif
Hongzhong Zhang
Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
International Journal of Molecular Sciences
toxicity
ionic liquids
acetylcholinesterase enzyme
extreme learning machine
multiple linear regression
author_facet Peng Zhu
Xuejing Kang
Yongsheng Zhao
Ullah Latif
Hongzhong Zhang
author_sort Peng Zhu
title Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
title_short Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
title_full Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
title_fullStr Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
title_full_unstemmed Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models
title_sort predicting the toxicity of ionic liquids toward acetylcholinesterase enzymes using novel qsar models
publisher MDPI AG
series International Journal of Molecular Sciences
issn 1422-0067
publishDate 2019-05-01
description Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using multiple linear regression (MLR) and extreme learning machine (ELM) algorithms. The structures of 57 cations and 21 anions were optimized using quantum chemistry calculations. The electrostatic potential surface area (<i>S</i><sub>EP</sub>) and the screening charge density distribution area (<i>S</i><sub>&#963;</sub>) descriptors were calculated and used for prediction of IL toxicity. Performance and predictive aptitude between MLR and ELM models were analyzed. Highest squared correlation coefficient (<i>R</i><sup>2</sup>), and also lowest average absolute relative deviation (AARD%) and root-mean-square error (RMSE) were observed for training set, test set, and total set for the ELM model. These findings validated the superior performance of ELM over the MLR toxicity prediction model.
topic toxicity
ionic liquids
acetylcholinesterase enzyme
extreme learning machine
multiple linear regression
url https://www.mdpi.com/1422-0067/20/9/2186
work_keys_str_mv AT pengzhu predictingthetoxicityofionicliquidstowardacetylcholinesteraseenzymesusingnovelqsarmodels
AT xuejingkang predictingthetoxicityofionicliquidstowardacetylcholinesteraseenzymesusingnovelqsarmodels
AT yongshengzhao predictingthetoxicityofionicliquidstowardacetylcholinesteraseenzymesusingnovelqsarmodels
AT ullahlatif predictingthetoxicityofionicliquidstowardacetylcholinesteraseenzymesusingnovelqsarmodels
AT hongzhongzhang predictingthetoxicityofionicliquidstowardacetylcholinesteraseenzymesusingnovelqsarmodels
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