Forschungszentrum Jülich GmbH, Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (IEK-11), Egerlandstr. 3, 91058, Erlangen, Germany.
Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Chemical Engineering, University of California, Santa Barbara, CA, 93106-5080, USA.
J Hazard Mater. 2018 Jun 15;352:17-26. doi: 10.1016/j.jhazmat.2018.03.025. Epub 2018 Mar 15.
Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S) and charge distribution area (S) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot.
大规模应用离子液体 (ILs) 取决于可设计和环保性质的发展。对于不同生物体和营养级别的离子液体潜在毒性的研究还不够充分。定量构效关系 (QSAR) 模型被应用于评估离子液体对白血病大鼠细胞系 (ICP-81) 的毒性。通过量子化学优化了 57 种阳离子和 21 种阴离子的结构。计算了静电势能表面积 (S) 和电荷分布面积 (S) 描述符，并用于预测 ILs 的毒性。分析并比较了极端学习机 (ELM) 模型与多元线性回归 (MLR) 和支持向量机 (SVM) 模型的性能和预测能力。观察到 ELM 在训练集、测试集和总集的最高 R 和最低 AARD%和 RMSE，这验证了 ELM 的性能优于 MLR 和 SVM 获得的性能。通过威廉姆斯图评估了模型的适用域。
