Predicting CCS via DMS Ion Intensity and ML
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Solution Overview
Problem
Current methods for predicting collision cross-section (CCS) values of unknown compounds rely on large numbers of molecular descriptors, making them complex and inefficient, with no empirical CCS library available for identification.
Innovation Solution
A system using a differential mobility spectrometry (DMS) device and machine learning algorithms to build a data model from known compounds' mass-to-charge ratios, separation voltages, and compensation voltages, allowing prediction of CCS values for unknown compounds by analyzing ion intensity variations across different voltage permutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational methods use large numbers of molecular descriptors to predict CCS values, then prediction accuracy can be improved, but method complexity increases significantly
Solution Approach 1:
The patent extracts and utilizes the DMS mobility difference parameter (μ*) as a key discriminating feature from experimental data, separating this critical parameter from the complex set of molecular descriptors. By focusing on this single extracted parameter combined with m/z ratio, the method achieves accurate CCS predictions without requiring large numbers of molecular descriptors, thus reducing method complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms the prediction approach by changing from using multiple molecular descriptors to using experimentally measured DMS mobility differences (Δμ*) and m/z ratios as input parameters. This parameter transformation enables the construction of simplified prediction models that maintain high accuracy by leveraging actual experimental mobility behavior rather than theoretical molecular properties.
2Adaptability or versatility
If no empirical CCS library is available, then compound identification capability is limited, but creating such a library requires extensive experimental data collection
Solution Approach 1:
The patent performs preliminary action by collecting experimental DMS data for a diverse set of reference compounds and using this data to train prediction models in advance. The trained models and predicted CCS values are then stored to create an empirical CCS library, enabling rapid compound identification without requiring extensive real-time data collection. This preliminary data collection and model training approach builds the identification capability upfront.
Solution Approach 2:
The patent creates copies of experimental DMS behavior patterns by training machine learning models on reference compound data. These models effectively copy the mobility behavior relationships, allowing the system to predict CCS values for new compounds based on learned patterns from the reference library, thus building identification capability without requiring physical measurement of every compound.
3Productivity
If traditional prediction methods are used, then computational resources can be reduced, but prediction effectiveness and reliability decrease
Solution Approach 1:
The patent replaces traditional computational prediction methods with an experimental measurement approach using DMS. Instead of relying on computationally intensive molecular dynamics simulations or complex quantum chemical calculations, the system uses experimental DMS mobility measurements combined with simple machine learning models. This substitution dramatically improves prediction effectiveness while reducing computational resource requirements, as the heavy computational burden is shifted to a one-time experimental data collection phase.
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
Enables simpler and more effective prediction of CCS values for unknown compounds, providing a third dimension for compound identification alongside retention time and exact mass, unaffected by sample matrix or experimental conditions.
Implementation Method 1
differential mobility spectrometry (DMS) device is used to determine how the intensities of their transmitted ions vary with a plurality of different separation voltages (SVs) and compensation voltages (CVs)
Data Source
AI summary
A plurality of known compounds with known CCS values is analyzed using a DMS device. The DMS device determines how the intensities of their transmitted ions vary with different separation voltages (SVs) and compensation voltages (CVs). A machine learning algorithm builds a data model from the known m/z value, known CCS value, and measured pairs of CV and SV values that provide optimal transmission through the DMS device for each of the known compounds. An unknown compound with an unknown CCS value is then analyzed. The DMS device determines how the intensity of its ions varies with the same different SVs and CVs. Finally, the machine learning algorithm predicts the CCS value of the unknown compound from the data model, the known m/z of the unknown compound, and the measured pairs of CV and SV values that provide optimal transmission through the DMS device for the unknown compound.