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massspecgym-ranker-infonce – AI Model by abrilrisso | AlphaNeural AI
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biology
mass-spectrometry
feature-extraction
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mit
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MassSpecGym Ranker: Contrastive Spectral Transformer
This model is a
Spectral Transformer
trained for high-precision molecular identification from MS/MS spectra. It is part of the
MassSpecGym
benchmark.
Model Details
Architecture:
Transformer Encoder (2 layers, 4 heads) with Fourier Feature m/z encoding.
Objective:
Contrastive Learning (InfoNCE) with a temperature of 0.1.
Input:
MS/MS fragment peaks (m/z and intensity) + Precursor mass.
Output:
4096-dimensional molecular embedding for candidate ranking.
Performance (MassSpecGym Test Set)
The model significantly outperforms standard MLP baselines:
Hit@1:
8.38%
Hit@5:
20.35%
Hit@20:
42.00%
Key Features
Fourier Features:
Captures high-precision mass differences essential for isotope identification.
Precursor Injection:
Provides global context to every spectral peak.
Attention Pooling:
Dynamically weights diagnostic peaks while down-weighting noise.
Usage
For full implementation, training scripts, and inference notebooks, visit the
GitHub Repository
.
