pyMSscreen: an interactive interface for automated quality control and prescreening in mass spectrometry workflows.

Reliable quality control of mass spectrometry data remains a major bottleneck in target, suspect and non-target screening workflows. In non-target analysis, thousands of features can be extracted through automated peak picking, many of which require manual verification to exclude artefacts arising from noise, co-elution, inconsistent chromatographic behavior or statistical prioritization. Similarly, suspect screening workflows depend on effective chromatographic and spectral quality control to support confidence in annotated features. However, existing quality control practices are often fragmented across vendor software and custom scripts, limiting reproducibility and scalability. This article describes pyMSscreen, a unified framework that enables automated and reproducible chromatographic quality control across target, suspect, and non-target mass spectrometry workflows. The backend leverages RDKit and pyOpenMS for compound validation and spectral extraction, while the frontend provides a web-based interface for systematic inspection of chromatograms and mass spectra. The development of pyMSscreen draws on experience gained from the earlier R-based Shinyscreen, which showed that accessible graphical user interface can greatly simplify the review of liquid chromatography mass spectrometry data. However, the practical limitations of the Shiny framework, including difficulties with deployment, motivated the creation of a more flexible and modern solution. pyMSscreen employs a modular architecture that supports target, suspect, and non-target screening by accommodating both SMILES or m/z only input lists. Automated quality control metrics are applied consistently across workflows, enabling assessment of peak shape, signal intensity, retention-time alignment, and background noise prior to downstream interpretation. Container-based deployment ensures reproducible installation and execution across computational environments. The performance and applicability of pyMSscreen is illustrated using a representative non-target dataset and suspect screening workflow demonstrating consistent results relative to established approaches, while substantially reducing manual data handling and review time. Overall, pyMSscreen provides an efficient, transparent, and reproducible platform for chromatographic quality control and data curation in high resolution mass spectrometry based cheminformatics workflows. The code is openly available (Artistic 2.0 license) at https://gitlab.com/uniluxembourg/lcsb/eci/pymsscreen .Scientific contributionpyMSscreen presents a unified framework for chromatographic quality control that integrates automated quality metrics with interactive visual inspection of mass spectrometry data. The central contribution is the consistent evaluation of chromatographic peak quality and spectral alignment across target, suspect, and non-target screening workflows within a single methodology. This work builds on earlier developments in interactive mass spectrometry data review while establishing a scalable and reproducible approach to feature-level quality control.