Methods and systems for using machine-learning models to estimate peptide-retention time
US12362041B1 · kind B1 · utility
Assignee
Inventor
Key dates
| Filing date | Jul 1, 2019 |
| Grant date | Jul 15, 2025 |
| Priority date | — |
| Expiry date | May 15, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01N2030/8831
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
The present disclosure relates to a machine-learning computing system for training and running a machine-learning model to estimate peptide-retention time for a sample. The machine-learning model can be configured to process inputs that characterize an individual peptide and/or amino acids in the peptide and to output an estimated retention time within a liquid-chromatography column for the peptide. The machine-learning model can include an encoder-decoder model. The encoder and/or the decoder can include a neural network. A subset of peptides can then be identified that are associated with estimated retention times within a specific elution time period during which portion of the sample was eluted from a chromatography column, and mass-spectrometry data can be analyzed to determine which of the subset of peptides are present within the sample.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.