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[2602.22822] FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics
Summary
FlexMS is a new framework designed for benchmarking deep learning models used in mass spectrum prediction within metabolomics, addressing challenges in model evaluation.
Why It Matters
This framework is significant as it provides a standardized approach for assessing various deep learning architectures in metabolomics, an area critical for drug discovery and material science. By facilitating better model comparisons, FlexMS can enhance the reliability and efficiency of molecular identification processes.
Key Takeaways
- FlexMS offers a flexible framework for benchmarking deep learning models in metabolomics.
- It addresses the challenges of heterogeneity in model evaluation and the lack of standardized benchmarks.
- The framework supports dynamic construction of model architectures and performance assessment using public datasets.
- Insights into factors affecting model performance are provided, aiding in model selection.
- Retrieval benchmarks simulate practical identification scenarios, enhancing the utility of the framework.
Computer Science > Artificial Intelligence arXiv:2602.22822 (cs) [Submitted on 26 Feb 2026] Title:FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics Authors:Yunhua Zhong, Yixuan Tang, Yifan Li, Jie Yang, Pan Liu, Jun Xia View a PDF of the paper titled FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics, by Yunhua Zhong and 5 other authors View PDF HTML (experimental) Abstract:The identification and property prediction of chemical molecules is of central importance in the advancement of drug discovery and material science, where the tandem mass spectrometry technology gives valuable fragmentation cues in the form of mass-to-charge ratio peaks. However, the lack of experimental spectra hinders the attachment of each molecular identification, and thus urges the establishment of prediction approaches for computational models. Deep learning models appear promising for predicting molecular structure spectra, but overall assessment remains challenging as a result of the heterogeneity in methods and the lack of well-defined benchmarks. To address this, our contribution is the creation of benchmark framework FlexMS for constructing and evaluating diverse model architectures in mass spectrum prediction. With its easy-to-use flexibility, FlexMS supports the dynamic construction of numerous distinct combinations of model architectures, while assessing their pe...
