Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects
Introduction: Development of drugs often fails due to toxicity and intolerable side effects. Recent advancements in the scientific community have rendered it possible to leverage machine learning techniques to predict individual side effects with domain knowledge features (i.e., drug classification)...
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| Format: | Article |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Drug Safety and Regulation |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdsfr.2023.1287535/full |
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| author | Han Jie Liu Jennifer L. Wilson |
| author_facet | Han Jie Liu Jennifer L. Wilson |
| author_sort | Han Jie Liu |
| collection | DOAJ |
| description | Introduction: Development of drugs often fails due to toxicity and intolerable side effects. Recent advancements in the scientific community have rendered it possible to leverage machine learning techniques to predict individual side effects with domain knowledge features (i.e., drug classification). While several factors can be used to anticipate drug effects including their targets, pathways, and drug classes, it is unclear which domain knowledge is most predictive and whether certain domain knowledge is more important than others for different side effects.Methods: The goal of this project is to understand the predictive values of drug targets, drug classification (i.e., level 2 ATC codes), and protein-protein interaction networks (i.e., PathFX targets and network proteins) for machine learning prediction of 30 frequently occurring drug-induced side effects.Results: We compared the prediction accuracy for individual side effects of trained models across five domain knowledge combinations and discovered that level 2 ATC codes have the highest predictive value across the domain knowledge features. Logistic regression coefficient analyses further suggest that side effects are more dependent on drug targets and drug classes, and less so on PathFX targets and network proteins.Discussion: Our quantitative assessments may inform the development of safe and effective drugs by understanding the domain knowledge features underlying frequently occurring drug-induced side effects. |
| format | Article |
| id | doaj-art-dc22104e18ca46cca47171f09a6eb8f6 |
| institution | OA Journals |
| issn | 2674-0869 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Drug Safety and Regulation |
| spelling | doaj-art-dc22104e18ca46cca47171f09a6eb8f62025-08-20T02:33:39ZengFrontiers Media S.A.Frontiers in Drug Safety and Regulation2674-08692023-11-01310.3389/fdsfr.2023.12875351287535Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effectsHan Jie LiuJennifer L. WilsonIntroduction: Development of drugs often fails due to toxicity and intolerable side effects. Recent advancements in the scientific community have rendered it possible to leverage machine learning techniques to predict individual side effects with domain knowledge features (i.e., drug classification). While several factors can be used to anticipate drug effects including their targets, pathways, and drug classes, it is unclear which domain knowledge is most predictive and whether certain domain knowledge is more important than others for different side effects.Methods: The goal of this project is to understand the predictive values of drug targets, drug classification (i.e., level 2 ATC codes), and protein-protein interaction networks (i.e., PathFX targets and network proteins) for machine learning prediction of 30 frequently occurring drug-induced side effects.Results: We compared the prediction accuracy for individual side effects of trained models across five domain knowledge combinations and discovered that level 2 ATC codes have the highest predictive value across the domain knowledge features. Logistic regression coefficient analyses further suggest that side effects are more dependent on drug targets and drug classes, and less so on PathFX targets and network proteins.Discussion: Our quantitative assessments may inform the development of safe and effective drugs by understanding the domain knowledge features underlying frequently occurring drug-induced side effects.https://www.frontiersin.org/articles/10.3389/fdsfr.2023.1287535/fullmachine learning (ML)drug developmentdrug safetydomain knowledge analysisdrug targetprotein-protein interaction (PPI) networks |
| spellingShingle | Han Jie Liu Jennifer L. Wilson Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects Frontiers in Drug Safety and Regulation machine learning (ML) drug development drug safety domain knowledge analysis drug target protein-protein interaction (PPI) networks |
| title | Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects |
| title_full | Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects |
| title_fullStr | Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects |
| title_full_unstemmed | Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects |
| title_short | Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects |
| title_sort | drug target class level and pathfx pathway information share utility for machine learning prediction of common drug induced side effects |
| topic | machine learning (ML) drug development drug safety domain knowledge analysis drug target protein-protein interaction (PPI) networks |
| url | https://www.frontiersin.org/articles/10.3389/fdsfr.2023.1287535/full |
| work_keys_str_mv | AT hanjieliu drugtargetclasslevelandpathfxpathwayinformationshareutilityformachinelearningpredictionofcommondruginducedsideeffects AT jenniferlwilson drugtargetclasslevelandpathfxpathwayinformationshareutilityformachinelearningpredictionofcommondruginducedsideeffects |