Towards Generalizable <i>In Silico</i> Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering
Bibliographic record
Abstract
Differential mobility spectrometry (DMS), a tool for separating chemically similar species (including isomers), is readily coupled to mass spectrometry to improve selectivity in analytical workflows. DMS dispersion curves, which describe the dynamic mobility experienced by an ion in a gaseous environment, show the maximum ion transmission for an analyte through the DMS instrument as a function of the separation voltage (SV) and compensation voltage (CV) conditions. To date, there exists no fast, general prediction tool for the dispersion behavior of ions. Here, we demonstrate a machine learning (ML) model that achieves generalized dispersion prediction using an in silico feature addition pipeline. We employ a data set containing 1141 dispersion curve measurements of anions and cations recorded in pure N 2 environments and in N 2 environments doped with 1.5% methanol (MeOH). Our feature addition pipeline can compute 1591 RDKit and Mordred descriptors using only SMILES codes, which are then normalized to sampled molecular distributions ( n = 100 000) using cumulative density functions (CDFs). This tool can be thought of as a “learned” feature fingerprint generation pipeline, which could be applied to almost any molecular (bio)cheminformatics tasks. Our best performing model, which for the first time considers solvent-modified environments, has a mean absolute error (MAE) of 2.1 ± 0.2 V for dispersion curve prediction, a significant improvement over the previous state-of-the-art work. We use explainability techniques ( e.g., SHAP analysis) to show that this feature addition pipeline is a semideterministic process for feature sets, and we discuss “best practices” to understand feature sets and maximize model performance. We expect that this tool could be used for prescreening to accelerate or even automate the use of DMS in complex analytical workflows ( e.g., 2D LC×DMS separation) and perform automated identification of transmission windows and increase the “self-driving” potential of the instrument. We make our models available as a free and accessible tool at https://github.com/HopkinsLaboratory/DispersionCurveGUI .
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".