ROASMI: accelerating small molecule identification by repurposing retention data

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 mod...

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Vydáno v:	Journal of cheminformatics Ročník 17; číslo 1; s. 20 - 15
Hlavní autoři:	Sun, Fang-Yuan, Yin, Ying-Hao, Liu, Hui-Jun, Shen, Lu-Na, Kang, Xiu-Lin, Xin, Gui-Zhong, Liu, Li-Fang, Zheng, Jia-Yi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 14.02.2025 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Analysis Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Coupling (molecular) Datasets Deep learning Documentation and Information in Chemistry Improving Reproducibility and Reusability in the Journal of Cheminformatics Isomers Liquid chromatography Metabolomics Molecular modelling Prediction models Predictions Replicability Retention Retention order Sequences Small-molecule identification Theoretical and Computational Chemistry Mississippi Deep learning Metabolomics Small-molecule identification Retention order Replicability
ISSN:	1758-2946, 1758-2946
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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. Scientific Contribution Our work discovers the dependence of buffer pH on the replicability of retention sequences in RPLC systems. Building upon this mechanistic insight, we have constructed a generalizability-oriented retention order prediction model called ROASMI, which is capable of providing reliable predictions across heterogeneous datasets with diverse chromatographic and chemical spaces.
AbstractList	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. Our work discovers the dependence of buffer pH on the replicability of retention sequences in RPLC systems. Building upon this mechanistic insight, we have constructed a generalizability-oriented retention order prediction model called ROASMI, which is capable of providing reliable predictions across heterogeneous datasets with diverse chromatographic and chemical spaces. 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. 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. Scientific Contribution Our work discovers the dependence of buffer pH on the replicability of retention sequences in RPLC systems. Building upon this mechanistic insight, we have constructed a generalizability-oriented retention order prediction model called ROASMI, which is capable of providing reliable predictions across heterogeneous datasets with diverse chromatographic and chemical spaces. 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. 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. 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.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. 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. Keywords: Metabolomics, Retention order, Small-molecule identification, Replicability, Deep learning 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. Scientific Contribution Our work discovers the dependence of buffer pH on the replicability of retention sequences in RPLC systems. Building upon this mechanistic insight, we have constructed a generalizability-oriented retention order prediction model called ROASMI, which is capable of providing reliable predictions across heterogeneous datasets with diverse chromatographic and chemical spaces.
Audience	Academic
Author	Kang, Xiu-Lin Xin, Gui-Zhong Shen, Lu-Na Sun, Fang-Yuan Zheng, Jia-Yi Yin, Ying-Hao Liu, Li-Fang Liu, Hui-Jun
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Keywords	Deep learning Metabolomics Small-molecule identification Retention order Replicability
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Snippet	The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models... Abstract The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention...
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SubjectTerms	Analysis Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Coupling (molecular) Datasets Deep learning Documentation and Information in Chemistry Improving Reproducibility and Reusability in the Journal of Cheminformatics Isomers Liquid chromatography Metabolomics Molecular modelling Prediction models Predictions Replicability Retention Retention order Sequences Small-molecule identification Theoretical and Computational Chemistry
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Title	ROASMI: accelerating small molecule identification by repurposing retention data
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