QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application
Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attent...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412023002763 |
| _version_ | 1827923358398808064 |
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| author | Jia-Yun Xu Kun Wang Shu-Hui Men Yang Yang Quan Zhou Zhen-Guang Yan |
| author_facet | Jia-Yun Xu Kun Wang Shu-Hui Men Yang Yang Quan Zhou Zhen-Guang Yan |
| author_sort | Jia-Yun Xu |
| collection | DOAJ |
| description | Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R2 of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China’s BTEX water quality standards. |
| first_indexed | 2024-03-13T04:56:44Z |
| format | Article |
| id | doaj.art-71f48eee33d34e0a955695ce685f5f67 |
| institution | Directory Open Access Journal |
| issn | 0160-4120 |
| language | English |
| last_indexed | 2024-03-13T04:56:44Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj.art-71f48eee33d34e0a955695ce685f5f672023-06-18T05:00:22ZengElsevierEnvironment International0160-41202023-07-01177108003QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary applicationJia-Yun Xu0Kun Wang1Shu-Hui Men2Yang Yang3Quan Zhou4Zhen-Guang Yan5State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaNational Engineering Laboratory for Lake Pollution Control and Ecological Restoration, State Environment Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaChina Energy Longyuan Environmental Protection Co.,Ltd., Beijing 100039, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Corresponding author.Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R2 of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China’s BTEX water quality standards.http://www.sciencedirect.com/science/article/pii/S0160412023002763Bioconcentration factorBTEXMachine learningQSAR-QSIIR modelWater quality criteria |
| spellingShingle | Jia-Yun Xu Kun Wang Shu-Hui Men Yang Yang Quan Zhou Zhen-Guang Yan QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application Environment International Bioconcentration factor BTEX Machine learning QSAR-QSIIR model Water quality criteria |
| title | QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application |
| title_full | QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application |
| title_fullStr | QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application |
| title_full_unstemmed | QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application |
| title_short | QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application |
| title_sort | qsar qsiir based prediction of bioconcentration factor using machine learning and preliminary application |
| topic | Bioconcentration factor BTEX Machine learning QSAR-QSIIR model Water quality criteria |
| url | http://www.sciencedirect.com/science/article/pii/S0160412023002763 |
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