Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction
Abstract Fast and accurate spectral prediction plays a crucial role in molecular design within fields such as pharmaceutical and materials science. Nevertheless, predicting molecular spectra typically requires quantum chemistry calculations, posing significant challenges for fast predictions and hig...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01698-z |
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| author | Yuzhi Xu Daqian Bian Cheng-Wei Ju Fanyu Zhao Pujun Xie Yuanqing Wang Wei Hu Zhenrong Sun John Z. H. Zhang Tong Zhu |
| author_facet | Yuzhi Xu Daqian Bian Cheng-Wei Ju Fanyu Zhao Pujun Xie Yuanqing Wang Wei Hu Zhenrong Sun John Z. H. Zhang Tong Zhu |
| author_sort | Yuzhi Xu |
| collection | DOAJ |
| description | Abstract Fast and accurate spectral prediction plays a crucial role in molecular design within fields such as pharmaceutical and materials science. Nevertheless, predicting molecular spectra typically requires quantum chemistry calculations, posing significant challenges for fast predictions and high-throughput screening. In this paper, we propose an equivariant, fast, and robust model, named EnviroDetaNet, which integrates molecular environment information. EnviroDetaNet employs an E(3)-equivariant message-passing neural network combining intrinsic atomic properties, spatial features, and environmental information, allowing it to comprehensively capture both local and global molecular information. Compared to state-of-the-art machine learning models, EnviroDetaNet excels in various predictive tasks and maintains high accuracy even with a 50% reduction in training data, demonstrating strong generalization capabilities. Ablation studies confirm that molecular environment information is crucial for improving model stability and accuracy. EnviroDetaNet also shows outstanding performance in spectral predictions for complex molecular systems, making it a powerful tool for accelerating molecular discovery. |
| format | Article |
| id | doaj-art-07c9f95d205044efac95ebf1e4bac7d9 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-07c9f95d205044efac95ebf1e4bac7d92025-08-20T03:37:38ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111010.1038/s41524-025-01698-zPretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra predictionYuzhi Xu0Daqian Bian1Cheng-Wei Ju2Fanyu Zhao3Pujun Xie4Yuanqing Wang5Wei Hu6Zhenrong Sun7John Z. H. Zhang8Tong Zhu9Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal UniversityDepartment of Chemistry and Pritzker School of Molecular Engineering, The University of ChicagoDepartment of Chemistry and Pritzker School of Molecular Engineering, The University of ChicagoShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal UniversityDepartment of Chemistry, New York UniversityDepartment of Chemistry, New York UniversitySchool of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Science)State Key Laboratory of Precision Spectroscopy, and School of Physics and Electron Science, East China Normal UniversityShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal UniversityShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal UniversityAbstract Fast and accurate spectral prediction plays a crucial role in molecular design within fields such as pharmaceutical and materials science. Nevertheless, predicting molecular spectra typically requires quantum chemistry calculations, posing significant challenges for fast predictions and high-throughput screening. In this paper, we propose an equivariant, fast, and robust model, named EnviroDetaNet, which integrates molecular environment information. EnviroDetaNet employs an E(3)-equivariant message-passing neural network combining intrinsic atomic properties, spatial features, and environmental information, allowing it to comprehensively capture both local and global molecular information. Compared to state-of-the-art machine learning models, EnviroDetaNet excels in various predictive tasks and maintains high accuracy even with a 50% reduction in training data, demonstrating strong generalization capabilities. Ablation studies confirm that molecular environment information is crucial for improving model stability and accuracy. EnviroDetaNet also shows outstanding performance in spectral predictions for complex molecular systems, making it a powerful tool for accelerating molecular discovery.https://doi.org/10.1038/s41524-025-01698-z |
| spellingShingle | Yuzhi Xu Daqian Bian Cheng-Wei Ju Fanyu Zhao Pujun Xie Yuanqing Wang Wei Hu Zhenrong Sun John Z. H. Zhang Tong Zhu Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction npj Computational Materials |
| title | Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction |
| title_full | Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction |
| title_fullStr | Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction |
| title_full_unstemmed | Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction |
| title_short | Pretrained E(3)-equivariant message-passing neural networks with multi-level representations for organic molecule spectra prediction |
| title_sort | pretrained e 3 equivariant message passing neural networks with multi level representations for organic molecule spectra prediction |
| url | https://doi.org/10.1038/s41524-025-01698-z |
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