From Reverse Phase Chromatography to HILIC: Graph Transformers Power Method-Independent Machine Learning of Retention Times
Bibliographic record
Abstract
Liquid chromatography (LC) is a cornerstone of analytical separations, but comparing the retention times (RTs) across different LC methods is challenging because of variations in experimental parameters such as column type and solvent gradient. Nevertheless, RTs are powerful metrics in tandem mass spectrometry (MS 2 ) that can reduce false positive rates for metabolite annotation, differentiate isobaric species, and improve peptide identification. Here, we present Graphormer-RT, a novel graph transformer that performs the first single-model method-independent prediction of RTs. We use the RepoRT data set, which contains 142,688 reverse phase (RP) RTs (from 191 methods) and 4,373 HILIC RTs (from 49 methods). Our best RP model (trained and tested on 191 methods) achieved a test set mean average error (MAE) of 29.3 ± 0.6 s, comparable performance to the state-of-the-art model which was only trained on a single LC method. Our best-performing HILIC model achieved a test MAE = 42.4 ± 2.9 s. We expect that Graphormer-RT can be used as an LC “foundation model”, where transfer learning can reduce the amount of training data needed for highly accurate “specialist” models applied to method-specific RP and HILIC tasks. These frameworks could enable the machine optimization of automated LC workflows, improved filtration of candidate structures using predicted RTs, and the in silico annotation of unknown analytes in LC-MS 2 measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".