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massspecgym-reconstructor-focal – AI Model by abrilrisso | AlphaNeural AI
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massspecgym-reconstructor-focal
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pytorch
biology
mass-spectrometry
feature-extraction
en
mit
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MassSpecGym Reconstructor: Fingerprint Prediction Transformer
This model is a
Spectral Transformer
optimized for molecular fingerprint reconstruction from MS/MS spectra. It treats identification as a multi-label classification task.
Model Details
Architecture:
Transformer Encoder (2 layers, 4 heads) with Fourier Feature m/z encoding.
Objective:
Focal Loss (gamma = 2.0) to address sparsity in molecular fingerprints.
Input:
MS/MS fragment peaks (m/z and intensity) + Precursor mass.
Output:
4096-dimensional binary fingerprint vector.
Performance (MassSpecGym Test Set)
The model focuses on structural fidelity and recovering rare active substructures:
Sample-wise F1-Score:
28.27%
Effectiveness:
Successfully avoids the "all-zero" prediction trap common in sparse chemical data.
Key Features
Focal Loss Optimization:
Specifically down-weights "easy negatives" (zero bits) to focus on rare structural fragments.
Isotope Awareness:
Uses Fourier Features to distinguish small mass shifts.
Attention Pooling:
Learns to ignore spectral noise and focus on diagnostic fragments.
Usage
For full implementation and evaluation details, visit the
GitHub Repository
.
