Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals
The metabolism of environmental organic chemicals often relies on the catalytic action of specific enzymes at the nanoscale, which is critical for assessing their environmental impact, safety, and efficacy. Hydrolysis is one of the primary metabolic and degradation reaction pathways. Traditionally,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/2/234 |
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| author | Zhe Liu Yufan Lin Qi He Lingjie Dai Qinyan Tan Binyan Jin Philip W. Lee Xiaoming Zhang Li Zhang |
| author_facet | Zhe Liu Yufan Lin Qi He Lingjie Dai Qinyan Tan Binyan Jin Philip W. Lee Xiaoming Zhang Li Zhang |
| author_sort | Zhe Liu |
| collection | DOAJ |
| description | The metabolism of environmental organic chemicals often relies on the catalytic action of specific enzymes at the nanoscale, which is critical for assessing their environmental impact, safety, and efficacy. Hydrolysis is one of the primary metabolic and degradation reaction pathways. Traditionally, hydrolysis product identification has relied on experimental methods that are both time-consuming and costly. In this study, machine-learning-based atomic-driven models were constructed to predict the hydrolysis reactions for environmental organic chemicals, including four main hydrolysis sites: N-Hydrolysis, O-Hydrolysis, C-Hydrolysis, and Global-Hydrolysis. These machine learning models were further integrated with a knowledge-based expert system to create a global hydrolysis model, which utilizes predicted hydrolysis site probabilities to prioritize potential hydrolysis products. For an external test set of 75 chemicals, the global hydrolysis site prediction model achieved an accuracy of 93%. Additionally, among 99 experimental hydrolysis products, our model successfully predicted 90, with a hit rate of 90%. This model offers significant potential for identifying hydrolysis metabolites in environmental organic chemicals. |
| format | Article |
| id | doaj-art-e5c91d7f2f774c4eac28733cbba6a4fd |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-e5c91d7f2f774c4eac28733cbba6a4fd2025-01-24T13:43:12ZengMDPI AGMolecules1420-30492025-01-0130223410.3390/molecules30020234Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic ChemicalsZhe Liu0Yufan Lin1Qi He2Lingjie Dai3Qinyan Tan4Binyan Jin5Philip W. Lee6Xiaoming Zhang7Li Zhang8Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaInnovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, ChinaThe metabolism of environmental organic chemicals often relies on the catalytic action of specific enzymes at the nanoscale, which is critical for assessing their environmental impact, safety, and efficacy. Hydrolysis is one of the primary metabolic and degradation reaction pathways. Traditionally, hydrolysis product identification has relied on experimental methods that are both time-consuming and costly. In this study, machine-learning-based atomic-driven models were constructed to predict the hydrolysis reactions for environmental organic chemicals, including four main hydrolysis sites: N-Hydrolysis, O-Hydrolysis, C-Hydrolysis, and Global-Hydrolysis. These machine learning models were further integrated with a knowledge-based expert system to create a global hydrolysis model, which utilizes predicted hydrolysis site probabilities to prioritize potential hydrolysis products. For an external test set of 75 chemicals, the global hydrolysis site prediction model achieved an accuracy of 93%. Additionally, among 99 experimental hydrolysis products, our model successfully predicted 90, with a hit rate of 90%. This model offers significant potential for identifying hydrolysis metabolites in environmental organic chemicals.https://www.mdpi.com/1420-3049/30/2/234machine learningexpert systemmetabolite evaluationhydrolysis siteshydrolysis products |
| spellingShingle | Zhe Liu Yufan Lin Qi He Lingjie Dai Qinyan Tan Binyan Jin Philip W. Lee Xiaoming Zhang Li Zhang Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals Molecules machine learning expert system metabolite evaluation hydrolysis sites hydrolysis products |
| title | Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals |
| title_full | Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals |
| title_fullStr | Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals |
| title_full_unstemmed | Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals |
| title_short | Atom-Driven and Knowledge-Based Hydrolysis Metabolite Assessment for Environmental Organic Chemicals |
| title_sort | atom driven and knowledge based hydrolysis metabolite assessment for environmental organic chemicals |
| topic | machine learning expert system metabolite evaluation hydrolysis sites hydrolysis products |
| url | https://www.mdpi.com/1420-3049/30/2/234 |
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