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In 2025 the Dagstuhl Seminar "Computational Proteomics" (25351), part of a series of Dagstuhl Seminars with the same name, brought together experts from proteomics, glycomics and machine learning to address key challenges in the field. Discussions emphasized the need for scalable and interoperable data infrastructures, a new initiative to generate large, AI-ready proteomics datasets, and community standards for reproducible and interpretable machine learning and harmonized glycomics workflows. Participants identified several barriers in clinical translation, multi-omics integration, and quantitative glyco-proteomics, highlighting limited data interoperability, heterogeneous experimental designs, and insufficient statistical and reporting frameworks. The seminar concluded with concrete action plans toward new standards, best practices, and collaborative initiatives to advance reproducible, sustainable and clinically relevant proteomics.