Reusability report: Annotating metabolite mass spectra with domain-inspired chemical formula transformers

We present an in-depth exploration of the Metabolite Inference with Spectrum Transformers (MIST) tool for annotating small molecule mass spectrometry (MS) data, focusing on its reproducibility and generalizability. MIST innovates by integrating a “chemical formula transformer” to process MS/MS spectra, aiming to bridge the substantial knowledge gap in untargeted MS studies, where only a fraction of spectra are confidently annotated. Here, we critically assess MIST’s reproducibility by following the tool’s original training and testing protocols, encountering minor challenges but largely succeeding in replicating results. We also evaluate MIST’s generalizability by applying it to an external dataset from the CASMI 2022 challenge, revealing insights into the model’s performance on previously unseen data. An ablation study further investigates the impact of various model features on database retrieval performance, suggesting that some algorithmic complexities may not significantly enhance performance. Through rigorous evaluation, this work underscores the challenges and considerations in developing robust computational tools for MS data analysis. We advocate for community-wide efforts in benchmarking, transparency, and data sharing to foster advancements in metabolomics and computational biology.