Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Wu, Ting; Li, Wanli; Chen, Mengyao; Zhou, Yanmei; Zhang, Qingyou

doi:10.1002/minf.202000102

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…The QSPR model has been proposed for the evaluation of the melting temperature of the imidazolium ILs [ 229 ]. The quantitative structural-activity relationship studies (QSAR) have been used for predicting the toxicity against human HeLa and MCF-7 cancer cell lines [ 230 ], the leukemia rat cell line (IPC-81) [ 231 , 232 ]. Zhu et al [ 233 ] elaborated quantitative structure-activity relationships (QSAR) models using an extreme learning machine (ELM) algorithm to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme.…”

Section: Design Of Environmentally Harmless Ilsmentioning

confidence: 99%

Ionic Liquids Toxicity—Benefits and Threats

Flieger

2020

IJMS

244

112

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Ionic liquids (ILs) are solvents with salt structures. Typically, they contain organic cations (ammonium, imidazolium, pyridinium, piperidinium or pyrrolidinium), and halogen, fluorinated or organic anions. While ILs are considered to be environmentally-friendly compounds, only a few reasons support this claim. This is because of high thermal stability, and negligible pressure at room temperature which makes them non-volatile, therefore preventing the release of ILs into the atmosphere. The expansion of the range of applications of ILs in many chemical industry fields has led to a growing threat of contamination of the aquatic and terrestrial environments by these compounds. As the possibility of the release of ILs into the environment s grow systematically, there is an increasing and urgent obligation to determine their toxic and antimicrobial influence on the environment. Many bioassays were carried out to evaluate the (eco)toxicity and biodegradability of ILs. Most of them have questioned their “green” features as ILs turned out to be toxic towards organisms from varied trophic levels. Therefore, there is a need for a new biodegradable, less toxic “greener” ILs. This review presents the potential risks to the environment linked to the application of ILs. These are the following: cytotoxicity evaluated by the use of human cells, toxicity manifesting in aqueous and terrestrial environments. The studies proving the relation between structures versus toxicity for ILs with special emphasis on directions suitable for designing safer ILs synthesized from renewable sources are also presented. The representants of a new generation of easily biodegradable ILs derivatives of amino acids, sugars, choline, and bicyclic monoterpene moiety are collected. Some benefits of using ILs in medicine, agriculture, and the bio-processing industry are also presented.

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Section: Design Of Environmentally Harmless Ilsmentioning

confidence: 99%

Ionic Liquids Toxicity—Benefits and Threats

Flieger

2020

IJMS

244

112

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“…Therefore, the SVM model can generally provide more accurate predictions on the toxicity of ILs against IPC-81, which confirms the conclusion obtained from Figure 7. The two ML models established in this work were compared with the reported models developed by Sosnowska et al [18] and Wu et al [19]. As shown in Table 1, the FNN and SVM models presented much lower RMSE and MAE as well as a much higher R 2 value than the two previous models, indicating their higher predictive accuracy.…”

Section: Model Comparisonmentioning

confidence: 77%

“…Due to the utilization of a larger database, this model, in principle, shows improved applicability. Based on this updated dataset, Wu et al [19] recently reported another MLR model using IL fingerprint descriptors, which resulted in an improved predictive accuracy (MAE = 0.34). Despite the effectiveness of MLR, there is still a large potential for the further improvement of model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Ionic Liquid Toxicity Prediction

2020

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In addition to proper physicochemical properties, low toxicity is also desirable when seeking suitable ionic liquids (ILs) for specific applications. In this context, machine learning (ML) models were developed to predict the IL toxicity in leukemia rat cell line (IPC-81) based on an extended experimental dataset. Following a systematic procedure including framework construction, hyper-parameter optimization, model training, and evaluation, the feedforward neural network (FNN) and support vector machine (SVM) algorithms were adopted to predict the toxicity of ILs directly from their molecular structures. Based on the ML structures optimized by the five-fold cross validation, two ML models were established and evaluated using IL structural descriptors as inputs. It was observed that both models exhibited high predictive accuracy, with the SVM model observed to be slightly better than the FNN model. For the SVM model, the determination coefficients were 0.9289 and 0.9202 for the training and test sets, respectively. The satisfactory predictive performance and generalization ability make our models useful for the computer-aided molecular design (CAMD) of environmentally friendly ILs.

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“…Some examples of studies concerning the prediction of toxicity for selected chemicals as potential pollutants are summarized in Table 3. One of the most frequently predicted environmental parameters is toxicity, which may be noticed due to the visible trend in IL properties' prediction analysis as summarized above [74][75][76][77]. It is expressed by different endpoints towards various organisms.…”

Section: Properties (Prediction and Correlation)mentioning

confidence: 99%

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

Bystrzanowska

2020

Symmetry

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In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.

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Estimation of Ionic Liquids Toxicity against Leukemia Rat Cell Line IPC‐81 based on the Empirical‐like Models using Intuitive and Explainable Fingerprint Descriptors

Cited by 15 publications

References 30 publications

Ionic Liquids Toxicity—Benefits and Threats

Ionic Liquids Toxicity—Benefits and Threats

Machine Learning for Ionic Liquid Toxicity Prediction

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

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