MultiChem: predicting chemical properties using multi-view graph attention network
Abstract Background Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-e...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BioData Mining |
| Online Access: | https://doi.org/10.1186/s13040-024-00419-4 |
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| _version_ | 1832594976025870336 |
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| author | Heesang Moon Mina Rho |
| author_facet | Heesang Moon Mina Rho |
| author_sort | Heesang Moon |
| collection | DOAJ |
| description | Abstract Background Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures. Leveraging this progress, we developed a novel multi-view learning model. Results We introduce a graph-integrated model that captures both local and global structural features of chemical compounds. In our model, graph attention layers are employed to effectively capture essential local structures by jointly considering atom and bond features, while multi-head attention layers extract important global features. We evaluated our model on nine MoleculeNet datasets, encompassing both classification and regression tasks, and compared its performance with state-of-the-art methods. Our model achieved an average area under the receiver operating characteristic (AUROC) of 0.822 and a root mean squared error (RMSE) of 1.133, representing a 3% improvement in AUROC and a 7% improvement in RMSE over state-of-the-art models in extensive seed testing. Conclusion MultiChem highlights the importance of integrating both local and global structural information in predicting molecular properties, while also assessing the stability of the models across multiple datasets using various random seed values. Implementation The codes are available at https://github.com/DMnBI/MultiChem . |
| format | Article |
| id | doaj-art-6c3fb036e00b4d3a93c3d7a6cc5f16be |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-6c3fb036e00b4d3a93c3d7a6cc5f16be2025-01-19T12:12:44ZengBMCBioData Mining1756-03812025-01-0118112110.1186/s13040-024-00419-4MultiChem: predicting chemical properties using multi-view graph attention networkHeesang Moon0Mina Rho1Department of Computer Science, Hanyang UniversityDepartment of Computer Science, Hanyang UniversityAbstract Background Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures. Leveraging this progress, we developed a novel multi-view learning model. Results We introduce a graph-integrated model that captures both local and global structural features of chemical compounds. In our model, graph attention layers are employed to effectively capture essential local structures by jointly considering atom and bond features, while multi-head attention layers extract important global features. We evaluated our model on nine MoleculeNet datasets, encompassing both classification and regression tasks, and compared its performance with state-of-the-art methods. Our model achieved an average area under the receiver operating characteristic (AUROC) of 0.822 and a root mean squared error (RMSE) of 1.133, representing a 3% improvement in AUROC and a 7% improvement in RMSE over state-of-the-art models in extensive seed testing. Conclusion MultiChem highlights the importance of integrating both local and global structural information in predicting molecular properties, while also assessing the stability of the models across multiple datasets using various random seed values. Implementation The codes are available at https://github.com/DMnBI/MultiChem .https://doi.org/10.1186/s13040-024-00419-4 |
| spellingShingle | Heesang Moon Mina Rho MultiChem: predicting chemical properties using multi-view graph attention network BioData Mining |
| title | MultiChem: predicting chemical properties using multi-view graph attention network |
| title_full | MultiChem: predicting chemical properties using multi-view graph attention network |
| title_fullStr | MultiChem: predicting chemical properties using multi-view graph attention network |
| title_full_unstemmed | MultiChem: predicting chemical properties using multi-view graph attention network |
| title_short | MultiChem: predicting chemical properties using multi-view graph attention network |
| title_sort | multichem predicting chemical properties using multi view graph attention network |
| url | https://doi.org/10.1186/s13040-024-00419-4 |
| work_keys_str_mv | AT heesangmoon multichempredictingchemicalpropertiesusingmultiviewgraphattentionnetwork AT minarho multichempredictingchemicalpropertiesusingmultiviewgraphattentionnetwork |