RT-Pred: A web server for accurate, customized liquid chromatography retention time prediction of chemicals
Bibliographic record
Abstract
• RT-Pred uses retention behavior of compounds to predict their retention times. • Uses advanced machine learning for accurate RT predictions and model creation. • Adaptable to any liquid chromatography setup for custom model building. • Achieves correlation coefficients of 0.95 (training) and 0.91 (validation). • Free, user-friendly, searchable and downloadable web-based tool. High-performance liquid chromatography (HPLC) together with mass spectrometry (MS) is routinely used to separate, identify and quantify chemicals. HPLC data also provides retention time (RT) which can be aligned with structural data. Recent developments in machine learning (ML) have improved our ability to predict RTs from known or postulated chemical structures, allowing RT data to be used more effectively in LC-MS-based compound identification. However, RT data is highly specific to each chromatographic method (CM) and hundreds of different CMs with interdependent parameters are used in separations. This has limited the application of ML-based RT predictions in compound identification. Here we introduce an easy-to-use RT prediction webserver (called RT-Pred) that predicts RTs for molecules across most chromatographic setups. RT-Pred not only supports its own in-house CM-specific RT predictors, it allows users to easily train a custom RT-Pred model using their own RT data on their own CM and to predict RTs with that custom model. RT-Pred also supports RT and compound searches against its own database of millions of predicted RTs spanning >40 different CMs. RT-Pred is also uniquely capable of accurately identifying compounds that will elute in the void volume or be retained on the column. Including this void/retained/eluted classifier significantly improves RT-Pred's performance. Tests indicate that RT-Pred had an average coefficient of determination (R²) of 0.95 over 20 different CMs. Comparisons of RT-Pred against other RT predictors showed that RT-Pred achieved lower mean absolute errors and higher R² scores than any other published RT predictor. RT-Pred is freely available at https://rtpred.ca .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.033 |
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".