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PepSpecBench: A Unified Evaluation Benchmark for Peptide Tandem Mass Spectrometry Prediction

NeurIPS 2026 · Probabilistic and scientific modeling › Molecules and proteins · approach: Protein interface and docking prediction · 0 citations (OpenAlex)

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Introduces PepSpecBench, a unified, backbone-disjoint, cross-species benchmark for peptide MS/MS fragment-intensity prediction that projects six baselines into a shared 234-dim ion space and probes robustness to NCE and charge perturbations.

Problem

Peptide MS/MS spectrum prediction lacks comparable evaluation: heterogeneous preprocessing and output spaces, leakage from random or modified-sequence splits, and little cross-species or metadata-sensitivity testing.

Approach

Harmonizes PROSPECT and MassIVE-KB under shared filters, enforces backbone-disjoint splits by hashing the naked peptide sequence, projects all model outputs into one canonical b/y ion space, and adds zero-shot cross-species OOD sets and NCE/charge perturbation probes.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: PROSPECT MassIVE-KB PXD014877 PXD027742

Benchmarks: PepSpecBench in-domain (MassIVE-KB, PROSPECT) PepSpecBench cross-species OOD suite (7 subsets)

Metrics: spectral angle (SA) spectral angle similarity (SAS) Pearson correlation (PCC)

Baselines: Prosit Prosit Transformer AlphaPeptDeep PredFull UniSpec FastSpel

Builds on: Prosit AlphaPeptDeep PredFull UniSpec FastSpel

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