Knowledge graph aids comprehensive explanation of drug and chemical toxicity
Abstract In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State‐of‐the‐art models are either limited by low accuracy, or lack of interpretability due to their black‐box nature. Here, we introduce AIDTox, an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12975 |
| _version_ | 1797742318685519872 |
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| author | Yun Hao Joseph D. Romano Jason H. Moore |
| author_facet | Yun Hao Joseph D. Romano Jason H. Moore |
| author_sort | Yun Hao |
| collection | DOAJ |
| description | Abstract In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State‐of‐the‐art models are either limited by low accuracy, or lack of interpretability due to their black‐box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical‐gene connections, gene‐pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints. |
| first_indexed | 2024-03-12T14:39:07Z |
| format | Article |
| id | doaj.art-66e48aa65fc149d7998f77758cf4e728 |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-03-12T14:39:07Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-66e48aa65fc149d7998f77758cf4e7282023-08-16T15:38:05ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062023-08-011281072107910.1002/psp4.12975Knowledge graph aids comprehensive explanation of drug and chemical toxicityYun Hao0Joseph D. Romano1Jason H. Moore2Genomics and Computational Biology (GCB) Graduate Program University of Pennsylvania Philadelphia Pennsylvania USAInstitute for Biomedical Informatics University of Pennsylvania Philadelphia Pennsylvania USADepartment of Computational Biomedicine Cedars‐Sinai Medical Center Los Angeles California USAAbstract In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State‐of‐the‐art models are either limited by low accuracy, or lack of interpretability due to their black‐box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical‐gene connections, gene‐pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.https://doi.org/10.1002/psp4.12975 |
| spellingShingle | Yun Hao Joseph D. Romano Jason H. Moore Knowledge graph aids comprehensive explanation of drug and chemical toxicity CPT: Pharmacometrics & Systems Pharmacology |
| title | Knowledge graph aids comprehensive explanation of drug and chemical toxicity |
| title_full | Knowledge graph aids comprehensive explanation of drug and chemical toxicity |
| title_fullStr | Knowledge graph aids comprehensive explanation of drug and chemical toxicity |
| title_full_unstemmed | Knowledge graph aids comprehensive explanation of drug and chemical toxicity |
| title_short | Knowledge graph aids comprehensive explanation of drug and chemical toxicity |
| title_sort | knowledge graph aids comprehensive explanation of drug and chemical toxicity |
| url | https://doi.org/10.1002/psp4.12975 |
| work_keys_str_mv | AT yunhao knowledgegraphaidscomprehensiveexplanationofdrugandchemicaltoxicity AT josephdromano knowledgegraphaidscomprehensiveexplanationofdrugandchemicaltoxicity AT jasonhmoore knowledgegraphaidscomprehensiveexplanationofdrugandchemicaltoxicity |