ML Mass Spectrometry Structure Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying small molecules from mass spectra are inefficient and inaccurate, particularly due to the complexity of small molecule structures and low signal-to-noise ratios, leading to missed viable drug candidates and high computational costs.
Innovation Solution
A machine learning model is trained to predict chemical structures from mass spectrometry data by generating fragmentation graphs and using probabilistic models to match compounds with their mass spectra, improving the accuracy and efficiency of small molecule identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based methods are used to predict fragmentation of molecules, then domain knowledge from chemistry is utilized, but many peaks in mass spectra cannot be explained
Solution Approach 1:
The patent replaces rule-based mechanical fragmentation prediction with a neural network-based system. The neural network learns fragmentation patterns from training data and predicts fragment masses and intensities, achieving better explanation of mass spectrum peaks while handling molecular complexity more effectively than traditional rule-based approaches.
2Productivity
If in-silico search of small molecule structure databases is performed, then millions of molecular structures can be searched, but the process is computationally intensive and time-consuming
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing fragmentation patterns and mass spectra for molecules in a database during an offline training phase. This pre-computed information is then used during online searching to rapidly compare experimental spectra against reference spectra, dramatically reducing computational resources and time required for actual compound identification.
Solution Approach 2:
The patent creates simplified representations (copies) of molecular fragmentation patterns and mass spectra that can be efficiently stored and compared. Instead of performing complex fragmentation calculations during each search, the system uses pre-generated spectral copies that enable fast matching and identification.
3Measurement precision
If mass spectra of small molecules are analyzed to identify compounds, then molecular structures can be determined, but the low signal-to-noise ratio leads to missed viable drug candidates
Solution Approach 1:
The patent implements feedback through iterative training of the neural network using experimentally measured mass spectra and corresponding molecular structures. The system learns from training data, refines its fragmentation predictions, and improves its ability to distinguish true signals from noise, thereby reducing false discoveries while enhancing detection sensitivity.
Data Source
AI summary
A method and system is for searching a database to identify structures of molecular compounds from mass spectrometry data. Operations of the method and system include receiving a query for a target molecular structure in the database, the query representing a query spectrum; accessing a machine learning model trained with molecule-spectrum pairs; inputting the query spectrum into the machine learning model; generating, from the machine learning model, a score for each of one or more molecular structures, each score representing a probability that a molecular structure corresponds to the query spectrum; selecting, based on each of the scores, a small molecule; and outputting, on a user interface, a representation of the small molecule.