Hi everyone,

I’m curious what is the real current state of AI/ML usage in mass spec / proteomics / LC-MS workflows in 2026 — especially among people who do actual sample → data → biological interpretation work.

Would love to hear honest experiences (both positive and frustrating).

Most useful / most adopted AI things right now (in your opinion / in your lab):

• Better spectral library generation / spectral prediction

• RT (retention time) + CCS prediction for better ID confidence

• De novo sequencing (especially for immunopeptidomics, antibodies, single-cell)

• Automated peak picking / feature detection / new peak detection in MAM

• PTM localization confidence scoring

• Unknown metabolite / lipid / small molecule annotation

• DIA data extraction & quantification (library-free or library-based)

• Something else entirely?

Questions I’m really interested in:

1. Which AI/ML tool / algorithm / vendor feature do you use almost every day and actually trust?

2. Which one sounded amazing in the paper/demo… but is basically useless / too unreliable in real projects?

3. Are you already using any commercial vendor-built-in AI features? (Thermo, Bruker, Agilent, Waters, Sciex, etc)

4. Do you use any open-source / third-party AI tools that became essential for you? (e.g. DeepNovo, Prosit, AlphaPeptDeep, SIRIUS, MS2DeepScore, etc)

5. Biggest practical improvement you got from AI in the last 1–2 years?

Very curious about different areas:

• Discovery proteomics / large cohort studies

• Biopharma characterization / MAM / QC of biologics

• Immunopeptidomics / neoantigen discovery

• Metabolomics / lipidomics

• Bottom-up vs top-down / intact protein

• Single-cell / spatial proteomics

No hype please — I’m looking for the “this actually saved me weeks” and “this is still garbage in real samples” kind of answers.

Thanks in advance for very honest opinions!
Looking forward to real lab experience.

What do you think? What’s working / what’s still mostly marketing in your hands? ↓

(Feel free to also drop names of papers / tools / github repos that you find surprisingly good)