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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

QSPR models to predict the physical hazards of mixtures: a state of art
This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).PMID:37706255 | DOI:10.1080/1062936X.20...
Source: SAR and QSAR in Environmental Research - September 14, 2023 Category: Environmental Health Authors: G Fayet P Rotureau Source Type: research

QSPR models to predict the physical hazards of mixtures: a state of art
This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).PMID:37706255 | DOI:10.1080/1062936X.20...
Source: SAR and QSAR in Environmental Research - September 14, 2023 Category: Environmental Health Authors: G Fayet P Rotureau Source Type: research

QSPR models to predict the physical hazards of mixtures: a state of art
This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).PMID:37706255 | DOI:10.1080/1062936X.20...
Source: SAR and QSAR in Environmental Research - September 14, 2023 Category: Environmental Health Authors: G Fayet P Rotureau Source Type: research

QSPR models to predict the physical hazards of mixtures: a state of art
This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).PMID:37706255 | DOI:10.1080/1062936X.20...
Source: SAR and QSAR in Environmental Research - September 14, 2023 Category: Environmental Health Authors: G Fayet P Rotureau Source Type: research

Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach
In this study, we propose a novel approach for the optimal selection of training set that enables a highly accurate prediction of the biodegradability of chemicals by QSAR. Our findings indicate that the proposed method effectively reduces the root mean squared error and improves the prediction accuracy.PMID:37674414 | DOI:10.1080/1062936X.2023.2251889 (Source: SAR and QSAR in Environmental Research)
Source: SAR and QSAR in Environmental Research - September 7, 2023 Category: Environmental Health Authors: K Takeda K Takeuchi Y Sakuratani K Kimbara Source Type: research

Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches
SAR QSAR Environ Res. 2023 Jul-Sep;34(9):689-708. doi: 10.1080/1062936X.2023.2250723. Epub 2023 Sep 7.ABSTRACTIn the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. M...
Source: SAR and QSAR in Environmental Research - September 7, 2023 Category: Environmental Health Authors: A Khan M Shahab F Nasir Y Waheed A Alshammari A Mohammad G Zichen R Li D Q Wei Source Type: research

Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach
In this study, we propose a novel approach for the optimal selection of training set that enables a highly accurate prediction of the biodegradability of chemicals by QSAR. Our findings indicate that the proposed method effectively reduces the root mean squared error and improves the prediction accuracy.PMID:37674414 | DOI:10.1080/1062936X.2023.2251889 (Source: SAR and QSAR in Environmental Research)
Source: SAR and QSAR in Environmental Research - September 7, 2023 Category: Environmental Health Authors: K Takeda K Takeuchi Y Sakuratani K Kimbara Source Type: research

Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches
SAR QSAR Environ Res. 2023 Sep 7:1-20. doi: 10.1080/1062936X.2023.2250723. Online ahead of print.ABSTRACTIn the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreov...
Source: SAR and QSAR in Environmental Research - September 7, 2023 Category: Environmental Health Authors: A Khan M Shahab F Nasir Y Waheed A Alshammari A Mohammad G Zichen R Li D Q Wei Source Type: research

Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach
In this study, we propose a novel approach for the optimal selection of training set that enables a highly accurate prediction of the biodegradability of chemicals by QSAR. Our findings indicate that the proposed method effectively reduces the root mean squared error and improves the prediction accuracy.PMID:37674414 | DOI:10.1080/1062936X.2023.2251889 (Source: SAR and QSAR in Environmental Research)
Source: SAR and QSAR in Environmental Research - September 7, 2023 Category: Environmental Health Authors: K Takeda K Takeuchi Y Sakuratani K Kimbara Source Type: research

Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches
SAR QSAR Environ Res. 2023 Sep 7:1-20. doi: 10.1080/1062936X.2023.2250723. Online ahead of print.ABSTRACTIn the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreov...
Source: SAR and QSAR in Environmental Research - September 7, 2023 Category: Environmental Health Authors: A Khan M Shahab F Nasir Y Waheed A Alshammari A Mohammad G Zichen R Li D Q Wei Source Type: research