Interpretation of chemical reaction yields with graph neural additive network
Prediction of chemical yields is crucial for exploring untapped chemical reactions and optimizing synthetic pathways for targeted compounds. Recently, graph neural networks have proven successful in achieving high predictive accuracy. However, they remain intrinsically black-box models, offering lim...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/addfaa |
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| _version_ | 1849723311279308800 |
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| author | Youngchun Kwon Yongsik Jung Youn-Suk Choi Seokho Kang |
| author_facet | Youngchun Kwon Yongsik Jung Youn-Suk Choi Seokho Kang |
| author_sort | Youngchun Kwon |
| collection | DOAJ |
| description | Prediction of chemical yields is crucial for exploring untapped chemical reactions and optimizing synthetic pathways for targeted compounds. Recently, graph neural networks have proven successful in achieving high predictive accuracy. However, they remain intrinsically black-box models, offering limited interpretability. Understanding how each reaction component contributes to the yield of a chemical reaction can help identify critical factors driving the success or failure of reactions, thereby potentially revealing opportunities for yield optimization. In this study, we present a novel method for interpretable chemical reaction yield prediction, which represents the yield of a chemical reaction as a simple summation of component-wise contributions from individual reaction components. To build an interpretable prediction model, we introduce a graph neural additive network architecture, wherein shared neural networks process individual reaction components in an input reaction while leveraging a reaction-level embedding to derive their respective contributions. The predicted yield is obtained by summing these component-wise contributions. The model is trained using a learning objective designed to effectively quantify the contributions of individual components by amplifying the influence of significant components and suppressing that of less influential components. The experimental results on benchmark datasets demonstrated that the proposed method achieved both high predictive accuracy and interpretability, making it suitable for practical use in synthetic pathway design for real-world applications. |
| format | Article |
| id | doaj-art-9a2dfe49ee194ef8a3ad83de6c03ea7a |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-9a2dfe49ee194ef8a3ad83de6c03ea7a2025-08-20T03:11:03ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202505410.1088/2632-2153/addfaaInterpretation of chemical reaction yields with graph neural additive networkYoungchun Kwon0https://orcid.org/0000-0001-7911-3670Yongsik Jung1https://orcid.org/0000-0003-4496-2351Youn-Suk Choi2https://orcid.org/0000-0001-7119-8788Seokho Kang3https://orcid.org/0000-0002-0960-0294Samsung Advanced Institute of Technology , Samsung Electronics Co. Ltd 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of KoreaSamsung Advanced Institute of Technology , Samsung Electronics Co. Ltd 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of KoreaSamsung Advanced Institute of Technology , Samsung Electronics Co. Ltd 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of KoreaDepartment of Industrial Engineering , Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of KoreaPrediction of chemical yields is crucial for exploring untapped chemical reactions and optimizing synthetic pathways for targeted compounds. Recently, graph neural networks have proven successful in achieving high predictive accuracy. However, they remain intrinsically black-box models, offering limited interpretability. Understanding how each reaction component contributes to the yield of a chemical reaction can help identify critical factors driving the success or failure of reactions, thereby potentially revealing opportunities for yield optimization. In this study, we present a novel method for interpretable chemical reaction yield prediction, which represents the yield of a chemical reaction as a simple summation of component-wise contributions from individual reaction components. To build an interpretable prediction model, we introduce a graph neural additive network architecture, wherein shared neural networks process individual reaction components in an input reaction while leveraging a reaction-level embedding to derive their respective contributions. The predicted yield is obtained by summing these component-wise contributions. The model is trained using a learning objective designed to effectively quantify the contributions of individual components by amplifying the influence of significant components and suppressing that of less influential components. The experimental results on benchmark datasets demonstrated that the proposed method achieved both high predictive accuracy and interpretability, making it suitable for practical use in synthetic pathway design for real-world applications.https://doi.org/10.1088/2632-2153/addfaachemical reaction yield predictiongraph neural networkinterpretable machine learningneural additive model |
| spellingShingle | Youngchun Kwon Yongsik Jung Youn-Suk Choi Seokho Kang Interpretation of chemical reaction yields with graph neural additive network Machine Learning: Science and Technology chemical reaction yield prediction graph neural network interpretable machine learning neural additive model |
| title | Interpretation of chemical reaction yields with graph neural additive network |
| title_full | Interpretation of chemical reaction yields with graph neural additive network |
| title_fullStr | Interpretation of chemical reaction yields with graph neural additive network |
| title_full_unstemmed | Interpretation of chemical reaction yields with graph neural additive network |
| title_short | Interpretation of chemical reaction yields with graph neural additive network |
| title_sort | interpretation of chemical reaction yields with graph neural additive network |
| topic | chemical reaction yield prediction graph neural network interpretable machine learning neural additive model |
| url | https://doi.org/10.1088/2632-2153/addfaa |
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