ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment
Abstract Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60989-7 |
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| _version_ | 1849402095703162880 |
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| author | Jiang Lu Lianlian Wu Ruijiang Li Mengxuan Wan Jun Yang Peng Zan Hui Bai Song He Xiaochen Bo |
| author_facet | Jiang Lu Lianlian Wu Ruijiang Li Mengxuan Wan Jun Yang Peng Zan Hui Bai Song He Xiaochen Bo |
| author_sort | Jiang Lu |
| collection | DOAJ |
| description | Abstract Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations and accurately predict data-scarce endpoints. Here we propose a machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges. ToxACoL models endpoint associations via graph topology and achieves knowledge transfer via graph convolution. The adjoint correlation mechanism encodes compounds and endpoints synchronously, yielding endpoint-aware and task-focused representations. Comprehensive analyses demonstrate that ToxACoL yields 43%-87% improvements for data-scarce human endpoints, while reducing training data by 70% to 80%. Visualization of the learned top-level representation interprets structural alert mechanisms. Filled-in toxicity values highlight potential for extrapolating animal results to humans. Finally, we deploy ToxACoL as a free web platform for rapid prediction of multi-condition acute toxicities. |
| format | Article |
| id | doaj-art-f3231c70bb19475caabc47f925d321d6 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-f3231c70bb19475caabc47f925d321d62025-08-20T03:37:37ZengNature PortfolioNature Communications2041-17232025-07-0116111910.1038/s41467-025-60989-7ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessmentJiang Lu0Lianlian Wu1Ruijiang Li2Mengxuan Wan3Jun Yang4Peng Zan5Hui Bai6Song He7Xiaochen Bo8Academy of Medical Engineering and Translational Medicine, Tianjin UniversityAcademy of Medical Engineering and Translational Medicine, Tianjin UniversityDepartment of Advanced & Interdisciplinary Biotechnology, Academy of Military Medical SciencesShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai UniversityDepartment of Cell Biology, School of Life Sciences, Central South UniversityShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai UniversityClinical Translational Research Center, Beijing Tsinghua Changgung Hospital, Tsinghua UniversityDepartment of Advanced & Interdisciplinary Biotechnology, Academy of Military Medical SciencesAcademy of Medical Engineering and Translational Medicine, Tianjin UniversityAbstract Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations and accurately predict data-scarce endpoints. Here we propose a machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges. ToxACoL models endpoint associations via graph topology and achieves knowledge transfer via graph convolution. The adjoint correlation mechanism encodes compounds and endpoints synchronously, yielding endpoint-aware and task-focused representations. Comprehensive analyses demonstrate that ToxACoL yields 43%-87% improvements for data-scarce human endpoints, while reducing training data by 70% to 80%. Visualization of the learned top-level representation interprets structural alert mechanisms. Filled-in toxicity values highlight potential for extrapolating animal results to humans. Finally, we deploy ToxACoL as a free web platform for rapid prediction of multi-condition acute toxicities.https://doi.org/10.1038/s41467-025-60989-7 |
| spellingShingle | Jiang Lu Lianlian Wu Ruijiang Li Mengxuan Wan Jun Yang Peng Zan Hui Bai Song He Xiaochen Bo ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment Nature Communications |
| title | ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment |
| title_full | ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment |
| title_fullStr | ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment |
| title_full_unstemmed | ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment |
| title_short | ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment |
| title_sort | toxacol an endpoint aware and task focused compound representation learning paradigm for acute toxicity assessment |
| url | https://doi.org/10.1038/s41467-025-60989-7 |
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