Mass Spectra Tokenization for Chemical Structure Prediction
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Solution Overview
Problem
Current mass spectrometry techniques face challenges in determining the chemical structure of molecules from mass spectra due to high chemical structure diversity and noisy data, making it difficult to confidently identify molecules, especially in complex samples.
Innovation Solution
A computational metabolomics platform utilizing trained bidirectional transformer-based machine-learning models to predict and generate chemical structures and properties from known mass spectrometry data, including mass-to-charge (m/z) values and precursor mass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mass spectrometry is used to determine chemical structure, then molecular identification is achieved, but accuracy decreases due to high chemical structure diversity and noisy data
Solution Approach 1:
The patent introduces an intermediate computational processing layer between mass spectrometry data acquisition and molecular identification. This intermediary system uses machine learning models to process raw MS data, filter noise, and generate predicted chemical structures, thereby improving both measurement precision and identification confidence without requiring direct interpretation of noisy spectral data
Solution Approach 2:
The patent replaces traditional manual or rule-based chemical structure determination methods with automated machine learning-based prediction systems. This substitution enables more accurate and reliable molecular identification by using computational algorithms to interpret mass spectrometry data, overcoming the limitations of human analysis and traditional spectral interpretation methods
2Productivity
If traditional mass spectrometry analysis is used, then molecular data is obtained, but time consumption increases due to the need to isolate and analyze each molecule individually
Solution Approach 1:
The patent creates a universal computational platform that can simultaneously analyze and predict structures for multiple different molecules from mass spectrometry data. This multi-functional system processes complex mixtures in parallel, eliminating the need for sequential isolation and analysis of individual compounds, thereby dramatically increasing productivity while reducing time loss
Solution Approach 2:
The patent performs preliminary computational processing of mass spectrometry data to predict chemical structures before detailed analysis is required. By pre-processing the data and generating structure predictions upfront, the system enables faster subsequent analysis and reduces the overall time needed for molecular characterization of complex samples
Data Source
AI summary
Methods for identifying a chemical structure of a compound based on mass spectrometry (MS) data using one or more computing devices are disclosed. The methods include receiving mass spectrometry (MS) data that includes a plurality of mass-to-charge values associated with fragments obtained from mass spectrometry performed on the compound, inputting the plurality of mass-to-charge values into a tokenizer trained to generate a plurality of tokens based on the plurality of mass-to-charge values, and determining one or more chemical structures of the compound based at least in part on the plurality of tokens.