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Article

Machine Learning for Ionic Liquid Toxicity Prediction

by 1, 2 and 1,2,*
1
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
2
Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
Processes 2021, 9(1), 65; https://doi.org/10.3390/pr9010065
Received: 10 December 2020 / Revised: 25 December 2020 / Accepted: 28 December 2020 / Published: 30 December 2020
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. View Full-Text
Keywords: ionic liquid; toxicity; machine learning; neural network; support vector machine ionic liquid; toxicity; machine learning; neural network; support vector machine
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MDPI and ACS Style

Wang, Z.; Song, Z.; Zhou, T. Machine Learning for Ionic Liquid Toxicity Prediction. Processes 2021, 9, 65. https://doi.org/10.3390/pr9010065

AMA Style

Wang Z, Song Z, Zhou T. Machine Learning for Ionic Liquid Toxicity Prediction. Processes. 2021; 9(1):65. https://doi.org/10.3390/pr9010065

Chicago/Turabian Style

Wang, Zihao, Zhen Song, and Teng Zhou. 2021. "Machine Learning for Ionic Liquid Toxicity Prediction" Processes 9, no. 1: 65. https://doi.org/10.3390/pr9010065

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