ROASMI: accelerating small molecule identification by repurposing retention data
Abstract The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models hold promise in addressing this issue, their predictive reliability is limited by uncertain generalizability. Here, we present the R...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00968-8 |
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| author | Fang-Yuan Sun Ying-Hao Yin Hui-Jun Liu Lu-Na Shen Xiu-Lin Kang Gui-Zhong Xin Li-Fang Liu Jia-Yi Zheng |
| author_facet | Fang-Yuan Sun Ying-Hao Yin Hui-Jun Liu Lu-Na Shen Xiu-Lin Kang Gui-Zhong Xin Li-Fang Liu Jia-Yi Zheng |
| author_sort | Fang-Yuan Sun |
| collection | DOAJ |
| description | Abstract The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models hold promise in addressing this issue, their predictive reliability is limited by uncertain generalizability. Here, we present the ROASMI model, which enables reliable prediction of retention order within a well-defined application domain by coupling data-driven molecular representation and mechanistic insights. The generalizability of ROASMI is proven by 71 independent reversed-phase liquid chromatography (RPLC) datasets. The application of ROASMI to four real-world datasets demonstrates its advantages in distinguishing coexisting isomers with similar fragmentation patterns and in annotating detection peaks without informative spectra. ROASMI is flexible enough to be retrained with user-defined reference sets and is compatible with other MS/MS scorers, making further improvements in small-molecule identification. |
| format | Article |
| id | doaj-art-7cc5c6c80e664b2e9c8dd3eeb42d49eb |
| institution | DOAJ |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-7cc5c6c80e664b2e9c8dd3eeb42d49eb2025-08-20T03:00:58ZengBMCJournal of Cheminformatics1758-29462025-02-0117111510.1186/s13321-025-00968-8ROASMI: accelerating small molecule identification by repurposing retention dataFang-Yuan Sun0Ying-Hao Yin1Hui-Jun Liu2Lu-Na Shen3Xiu-Lin Kang4Gui-Zhong Xin5Li-Fang Liu6Jia-Yi Zheng7State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityState Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical UniversityAbstract The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models hold promise in addressing this issue, their predictive reliability is limited by uncertain generalizability. Here, we present the ROASMI model, which enables reliable prediction of retention order within a well-defined application domain by coupling data-driven molecular representation and mechanistic insights. The generalizability of ROASMI is proven by 71 independent reversed-phase liquid chromatography (RPLC) datasets. The application of ROASMI to four real-world datasets demonstrates its advantages in distinguishing coexisting isomers with similar fragmentation patterns and in annotating detection peaks without informative spectra. ROASMI is flexible enough to be retrained with user-defined reference sets and is compatible with other MS/MS scorers, making further improvements in small-molecule identification. https://doi.org/10.1186/s13321-025-00968-8MetabolomicsRetention orderSmall-molecule identificationReplicabilityDeep learning |
| spellingShingle | Fang-Yuan Sun Ying-Hao Yin Hui-Jun Liu Lu-Na Shen Xiu-Lin Kang Gui-Zhong Xin Li-Fang Liu Jia-Yi Zheng ROASMI: accelerating small molecule identification by repurposing retention data Journal of Cheminformatics Metabolomics Retention order Small-molecule identification Replicability Deep learning |
| title | ROASMI: accelerating small molecule identification by repurposing retention data |
| title_full | ROASMI: accelerating small molecule identification by repurposing retention data |
| title_fullStr | ROASMI: accelerating small molecule identification by repurposing retention data |
| title_full_unstemmed | ROASMI: accelerating small molecule identification by repurposing retention data |
| title_short | ROASMI: accelerating small molecule identification by repurposing retention data |
| title_sort | roasmi accelerating small molecule identification by repurposing retention data |
| topic | Metabolomics Retention order Small-molecule identification Replicability Deep learning |
| url | https://doi.org/10.1186/s13321-025-00968-8 |
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