Automated Structural Elucidation of Small Molecule Components
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Solution Overview
Problem
The current manual process for structural elucidation of unnamed compounds in complex mixtures is laborious, time-consuming, and lacks efficiency, hindering the development of novel biomarkers and clinical interventions.
Innovation Solution
A method and apparatus for automated analysis and elucidation of small molecule components, utilizing precomputed tables, machine-learning approaches, and data aggregation from ion repositories and public sources to quickly generate possible molecular formulas and predict key structural features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of LC-MS1/MS2 data and database searches are performed, then structural elucidation can be achieved, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of spectral examination and database searching with an automated computational system. The system uses computer algorithms to automatically analyze LC-MS1/MS2 data, perform database searches, and generate structural candidates, eliminating the need for manual human analysis while maintaining or improving elucidation accuracy.
Solution Approach 2:
The system enables self-service structural elucidation by automatically processing spectral data without requiring human analysts. The computational workflow independently performs molecular formula generation, database searching, and structural candidate ranking, allowing the system to serve itself in the elucidation process.
2Device complexity
If manual spectral analysis is performed to reduce candidate compounds to 1-5 testable structures, then structural candidates can be identified, but the process requires great length and human expertise
Solution Approach 1:
The system changes the operational parameters by using automated computational algorithms instead of manual human judgment. It transforms the complex task of candidate stratification into a series of defined computational steps including molecular formula generation, spectral matching, and automated ranking, making the process easier to operate while maintaining the ability to handle complexity.
Solution Approach 2:
The patent segments the complex task of structural elucidation into distinct computational modules: (1) molecular formula generation from exact mass, (2) database searching and spectral matching, (3) structural candidate generation, and (4) automated ranking and stratification. This segmentation allows each module to be optimized independently and simplifies the overall operational process.
3Productivity
If automated analysis methods are implemented, then speed and productivity are enhanced, but the capability to accurately predict structural features must be developed
Solution Approach 1:
The system performs preliminary actions by pre-generating molecular formulas from exact mass data and pre-searching databases for matching compounds before final structural identification. This preliminary processing filters out unlikely candidates early, enabling faster automated analysis while maintaining accuracy in the final structural predictions.
Solution Approach 2:
The system implements feedback mechanisms where computational predictions of structural features are continuously refined based on matching quality metrics and spectral data. The automated workflow uses feedback from spectral matching results to adjust and improve structural candidate predictions, ensuring both speed and accuracy in the elucidation process.
Data Source
AI summary
A method of structurally elucidating small molecule components, comprises determining, per sample, a molecular mass (MM) of a candidate compound (CC) fragment, determining possible molecular formulas (MF) having the fragment MM, and aggregating MS2 spectra for each CC fragment to form a candidate CC MS2 spectrum. Possible MFs and compound structures of an ion in the candidate MS2 spectrum consistent with the possible fragment MFs are determined. Known compounds (KC) similar via MS2 spectrum to the CC. KCs having a compound structure plausibly corresponding to the CC MS2 spectrum, a probability of the MS2 spectrum per fragment having compound substructures, and a combination of known fragment spectra (KFS) forming a compound spectrum statistically similar to the candidate MS2 spectrum of the CC, are determined. The possible MFs and compound structures. KCs, compound substructures, and combination of KFS, are associated with the MS2 spectrum of the CC/fragments thereof.