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Correction
SAR QSAR Environ Res. 2023 Oct 13:1. doi: 10.1080/1062936X.2023.2266905. Online ahead of print.NO ABSTRACTPMID:37830182 | DOI:10.1080/1062936X.2023.2266905 (Source: SAR and QSAR in Environmental Research)
Source: SAR and QSAR in Environmental Research - October 13, 2023 Category: Environmental Health Source Type: research

Correction
SAR QSAR Environ Res. 2023 Oct 13:1. doi: 10.1080/1062936X.2023.2266905. Online ahead of print.NO ABSTRACTPMID:37830182 | DOI:10.1080/1062936X.2023.2266905 (Source: SAR and QSAR in Environmental Research)
Source: SAR and QSAR in Environmental Research - October 13, 2023 Category: Environmental Health Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research

Modelling enzyme inhibition toxicity of ionic liquid from molecular structure via convolutional neural network model
SAR QSAR Environ Res. 2023 Sep 18:1-15. doi: 10.1080/1062936X.2023.2255517. Online ahead of print.ABSTRACTDeep learning (DL) methods further promote the development of quantitative structure-activity/property relationship (QSAR/QSPR) models by dealing with complex relationships between data. An acetylcholinesterase inhibitory toxicity model of ionic liquids (ILs) was established using a convolution neural network (CNN) combined with support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP). A CNN model was proposed for feature self-learning and extraction of ILs. By comparing with the model results t...
Source: SAR and QSAR in Environmental Research - September 18, 2023 Category: Environmental Health Authors: R Zhang Y Chen D Fan T Liu Z Ma Y Dai Y Wang Z Zhu Source Type: research