ML Isotopic Pattern Classifier for Mass Spec Formula Annotation
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Solution Overview
Problem
Current mass spectrometric methods face challenges in efficiently determining the presence or absence of chemical elements in compounds due to the exponential growth of possible chemical formulas, making it difficult to annotate chemical formulas from measured isotopic patterns, especially for complex mixtures.
Innovation Solution
A mass spectrometric method using supervised element classifiers to represent isotopic patterns as feature vectors and classify the presence or absence of chemical elements, trained on compounds with known elemental composition, to reduce the complexity of possible chemical formulas and enhance annotation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mass spectrometric methods are used to determine chemical elements in compounds, then measurement capability is provided, but the exponential growth of possible chemical formulas makes annotation difficult and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning classifiers on isotopic patterns of compounds with known elemental composition before actual analysis. The classifiers are prepared in advance with learned patterns of element presence/absence based on isotopic distributions, enabling rapid annotation during actual mass spectrometry analysis without time-consuming combinatorial calculations at runtime
Solution Approach 2:
The patent replaces the mechanical/combinatorial system of systematically generating and comparing all possible chemical formulas with a machine learning-based classification system. Instead of exhaustively calculating isotopic patterns for numerous possible formulas, the ML classifiers directly predict element presence from measured isotopic patterns, dramatically reducing annotation time while maintaining accuracy
2Adaptability or versatility
If the set of chemical elements considered for generating chemical formulas is expanded, then the completeness of formula annotation is improved, but the number of possible chemical formulas grows exponentially
Solution Approach 1:
The patent extracts and identifies only the relevant chemical elements present in the analyte using machine learning classification of isotopic patterns. By determining which specific elements are actually present rather than considering all possible elements, the method reduces the effective set of elements needed for formula generation, thereby reducing combinatorial complexity while maintaining comprehensive element coverage
Solution Approach 2:
The patent changes the parameter of element set size dynamically. Instead of using a fixed large set of all possible elements, the ML classifiers identify and select only the subset of elements actually present in the compound based on isotopic pattern analysis. This adaptive parameter adjustment reduces calculation complexity while preserving the ability to detect all relevant elements
3Productivity
If machine learning classifiers are used to determine element presence, then the complexity of formula annotation is reduced, but training data and computational resources are required
Solution Approach 1:
The patent applies preliminary action by performing the computationally intensive training of machine learning classifiers in advance, before actual analytical work. The classifiers are trained on databases of compounds with known elemental composition and their isotopic patterns, so that during actual analysis, only fast classification predictions are needed, achieving high productivity without time-consuming calculations during measurement
Solution Approach 2:
The patent introduces machine learning classifiers as intermediary components between mass spectrometry measurement and chemical formula annotation. These classifiers act as a bridge that translates raw isotopic pattern data into element presence/absence predictions, simplifying the overall system architecture and enabling faster annotation while managing complexity through modular design
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively reduces the complexity of determining chemical elements present in analytes, improving the accuracy of chemical formula annotation by using machine learning to classify isotopic patterns, thereby narrowing down the set of possible chemical formulas for comparison.
Implementation Method 1
Mass spectrometry (MS) is a widely used analytical method for qualitative and quantitative identification of compounds
Implementation Method 2
converting compounds of a sample into the gas phase, ionizing the compounds in an ion source
Data Source
AI summary
The present invention relates to a mass spectrometric method for determining (predicting) the presence or absence of a chemical element in an analyte which provides valuable information towards reduction of complexity for annotating a chemical formula to the analyte. The method is based on representing a measured isotopic pattern of an analyte as a feature vector and assigning the feature vector to the presence/absence class using a machine learning algorithm, like a support vector machine (SVM) or an artificial neural network (NN).