Deep Learning Spectral Compression for Local Library Search
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Solution Overview
Problem
Existing MS/MS identification methods struggle with complex samples due to similar fragments, requiring significant compute power and memory, especially for small molecules, and current solutions are limited by library size or require cloud computing.
Innovation Solution
Utilizing a neural network encoder to compress spectral data from experimental and library/database mass spectra, enabling efficient comparison and identification of compounds with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional library search methods are used to identify compounds in complex samples, then identification can be performed, but compute power and memory requirements become prohibitively high
Solution Approach 1:
The patent transforms the spectral data from its original high-dimensional form into a compressed latent representation space using a pre-trained neural network encoder. This parameter transformation reduces the computational complexity of subsequent comparisons while preserving the essential information needed for accurate compound identification in complex samples
Solution Approach 2:
The patent creates a compressed copy (latent representation) of the spectral data that captures the essential features needed for identification. Instead of working with the full high-dimensional spectral data during library search, the system uses these compressed representations to achieve accurate identification with reduced computational resources
2Measurement precision
If traditional library search methods are used to identify compounds in complex samples, then identification can be performed, but memory requirements become prohibitively high
Solution Approach 1:
The patent applies a neural network encoder to transform spectral data into a compressed latent space, fundamentally changing the parameter dimensions from thousands of spectral points to a manageable number of latent features. This dramatically reduces the memory required to store and search spectral libraries while maintaining identification accuracy
Solution Approach 2:
The system creates compressed copies of spectral data in the form of latent representations. These compressed copies retain the essential information for compound identification but occupy significantly less memory space, enabling local library searches without requiring cloud computing resources
3Productivity
If spectral data is compressed using neural network encoder, then compute time and memory are reduced, but identification accuracy must be maintained
Solution Approach 1:
The patent performs preliminary encoding of spectral data using a pre-trained neural network encoder before the actual library search. This preprocessing step creates compressed representations that are optimized for subsequent comparison operations, ensuring that both compute efficiency and identification accuracy are maintained throughout the analysis pipeline
Solution Approach 2:
The transformation to latent space preserves the discriminative information necessary for accurate identification while reducing the dimensional complexity. The neural network encoder is trained to maintain the essential spectral features that differentiate compounds, ensuring that compression does not compromise identification accuracy
Data Source
AI summary
Known mass spectral data of a library of spectra corresponding to known compounds or known mass spectral data determined from a database of known compounds are compressed using a neural network encoder, producing a group of corresponding compressed known representations of known mass spectral data. Experimental mass spectral data of an experimental mass spectrum is compressed using the neural network encoder, producing a compressed experimental representation of the experimental mass spectral data. The experimental representation is compared to the group of known representations and each comparison is scored. At least one comparison with a score above a predetermined score threshold is selected. A known compound is determined from the selected at least one comparison. The known compound is identified as a compound of the experimental spectrum.