FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

Researchers have developed FlexMS, a flexible benchmark framework for evaluating deep learning models that predict mass spectra for molecular identification in drug discovery and material science. The framework addresses current challenges in assessing different prediction approaches by providing standardized evaluation methods and insights into performance factors across various model architectures.

• →FlexMS provides a standardized benchmark framework for comparing deep learning-based mass spectrum prediction tools in metabolomics research.
• →The framework supports dynamic construction of diverse model architectures and evaluates performance using preprocessed public datasets.
• →Research identifies key performance factors including structural diversity, hyperparameters, pretraining effects, and cross-domain transfer learning.
• →Retrieval benchmarks simulate real-world molecular identification scenarios using predicted spectra.
• →The tool addresses the current lack of experimental spectra that hinders molecular identification in drug discovery.