Learning motif features and topological structure of molecules for metabolic pathway prediction
Abstract Metabolites serve as crucial biomarkers for assessing disease progression and understanding underlying pathogenic mechanisms. However, when the metabolic pathway category of metabolites is unknown, researchers face challenges in conducting metabolomic analyses. Due to the complexity of wet...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | Journal of Cheminformatics |
| Online Access: | https://doi.org/10.1186/s13321-025-00994-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849311065130663936 |
|---|---|
| author | Jianguo Hu Yiqing Zhang Jinxin Xie Zhen Yuan Zhangxiang Yin Shanshan Shi Honglin Li Shiliang Li |
| author_facet | Jianguo Hu Yiqing Zhang Jinxin Xie Zhen Yuan Zhangxiang Yin Shanshan Shi Honglin Li Shiliang Li |
| author_sort | Jianguo Hu |
| collection | DOAJ |
| description | Abstract Metabolites serve as crucial biomarkers for assessing disease progression and understanding underlying pathogenic mechanisms. However, when the metabolic pathway category of metabolites is unknown, researchers face challenges in conducting metabolomic analyses. Due to the complexity of wet laboratory experimentation for pathway identification, there is a growing demand for predictive methods. Various computational approaches, including machine learning and graph neural networks, have been proposed; however, interpretability remains a challenge. We have developed a neural network framework called MotifMol3D, which is designed for predicting molecular metabolic pathway categories. This framework introduces motif information to mine local features of small-sample molecules, combining with graph neural network and 3D information to complete the prediction task. Using a dataset of 5,698 molecules that participate in 11 metabolic pathway categories in the KEGG database, MotifMol3D outperformed state-of-the-art methods in precision, recall, and F1 score. In addition, ablation study and motif analysis have demonstrated the effectiveness and usefulness of the model. Motif analysis, in particular, has shown motif information can actually characterize the main features of specific pathway molecules to a certain extent and enhance the interpretability of the model. An external validation further corroborates this observation. MotifMol3D is an open-source tool that is available at https://github.com/Irena-Zhang/MotifMol3D.git . Scientific contribution MotifMol3D integrates motif information, graph neural networks, and 3D structural data to enhance feature extraction for small-sample molecules, improving the precision and interpretability of metabolic pathway predictions. The model outperforms state-of-the-art approaches in precision, recall, and F1 score. This work reveals how motif information characterizes pathway-specific molecules, offering novel insights into molecular properties within metabolic pathways. |
| format | Article |
| id | doaj-art-d2f18ae56b8e4a1784d8618c9e2bcc69 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-d2f18ae56b8e4a1784d8618c9e2bcc692025-08-20T03:53:32ZengBMCJournal of Cheminformatics1758-29462025-04-0117111410.1186/s13321-025-00994-6Learning motif features and topological structure of molecules for metabolic pathway predictionJianguo Hu0Yiqing Zhang1Jinxin Xie2Zhen Yuan3Zhangxiang Yin4Shanshan Shi5Honglin Li6Shiliang Li7Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyShanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and TechnologyAbstract Metabolites serve as crucial biomarkers for assessing disease progression and understanding underlying pathogenic mechanisms. However, when the metabolic pathway category of metabolites is unknown, researchers face challenges in conducting metabolomic analyses. Due to the complexity of wet laboratory experimentation for pathway identification, there is a growing demand for predictive methods. Various computational approaches, including machine learning and graph neural networks, have been proposed; however, interpretability remains a challenge. We have developed a neural network framework called MotifMol3D, which is designed for predicting molecular metabolic pathway categories. This framework introduces motif information to mine local features of small-sample molecules, combining with graph neural network and 3D information to complete the prediction task. Using a dataset of 5,698 molecules that participate in 11 metabolic pathway categories in the KEGG database, MotifMol3D outperformed state-of-the-art methods in precision, recall, and F1 score. In addition, ablation study and motif analysis have demonstrated the effectiveness and usefulness of the model. Motif analysis, in particular, has shown motif information can actually characterize the main features of specific pathway molecules to a certain extent and enhance the interpretability of the model. An external validation further corroborates this observation. MotifMol3D is an open-source tool that is available at https://github.com/Irena-Zhang/MotifMol3D.git . Scientific contribution MotifMol3D integrates motif information, graph neural networks, and 3D structural data to enhance feature extraction for small-sample molecules, improving the precision and interpretability of metabolic pathway predictions. The model outperforms state-of-the-art approaches in precision, recall, and F1 score. This work reveals how motif information characterizes pathway-specific molecules, offering novel insights into molecular properties within metabolic pathways.https://doi.org/10.1186/s13321-025-00994-6 |
| spellingShingle | Jianguo Hu Yiqing Zhang Jinxin Xie Zhen Yuan Zhangxiang Yin Shanshan Shi Honglin Li Shiliang Li Learning motif features and topological structure of molecules for metabolic pathway prediction Journal of Cheminformatics |
| title | Learning motif features and topological structure of molecules for metabolic pathway prediction |
| title_full | Learning motif features and topological structure of molecules for metabolic pathway prediction |
| title_fullStr | Learning motif features and topological structure of molecules for metabolic pathway prediction |
| title_full_unstemmed | Learning motif features and topological structure of molecules for metabolic pathway prediction |
| title_short | Learning motif features and topological structure of molecules for metabolic pathway prediction |
| title_sort | learning motif features and topological structure of molecules for metabolic pathway prediction |
| url | https://doi.org/10.1186/s13321-025-00994-6 |
| work_keys_str_mv | AT jianguohu learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT yiqingzhang learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT jinxinxie learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT zhenyuan learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT zhangxiangyin learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT shanshanshi learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT honglinli learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction AT shiliangli learningmotiffeaturesandtopologicalstructureofmoleculesformetabolicpathwayprediction |