Literature DB >> 34315017

Atom surface fragment contribution method for predicting the toxicity of ionic liquids.

Xuejing Kang1, Yongsheng Zhao2, Zhongbing Chen3.   

Abstract

In this study, a novel method-atom surface fragment contribution (ASFC)-was proposed for assessing the properties of compounds. We developed a predictive model using the ASFC method based on the sigma surface areas (Sσ-surface) of fragments/groups for estimating the toxicity of ILs. A toxicity dataset of 140 ILs towards leukemia rat cell line (ICP-81) was gathered and employed to train and validate models. The Sσ-surface values of atoms in each group were firstly calculated from the COSMO profiles of cations and anions for ILs. Then the Sσ-surface values of 26 groups were obtained and used as input descriptors for modelling. The R2 and MSE of the built ASFC model were 0.924 and 0.071, respectively. Results indicate that the ASFC model developed by the new approach possesses great accuracy and reliability. In total, the ASFC method has extensive potential for the application of estimating diverse properties of ILs and other compounds due to its remarkable advantages.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Atom surface fragment contribution; Group contribution; Ionic liquids; Sigma surface area; Toxicity

Year:  2021        PMID: 34315017     DOI: 10.1016/j.jhazmat.2021.126705

Source DB:  PubMed          Journal:  J Hazard Mater        ISSN: 0304-3894            Impact factor:   10.588


  1 in total

1.  Deep Probabilistic Learning Model for Prediction of Ionic Liquids Toxicity.

Authors:  Mapopa Chipofya; Hilal Tayara; Kil To Chong
Journal:  Int J Mol Sci       Date:  2022-05-09       Impact factor: 6.208

  1 in total

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