Applications of machine learning for the general unknown screening of HRMS data within forensic toxicology
Bibliographic record
Abstract
This review is intended for forensic toxicologists and cheminformaticians seeking an understanding of the past implementations and future directions of artificial intelligence (AI) and machine learning (ML) for high-resolution mass spectrometry (HRMS) data interrogation in forensic toxicology. It provides a comprehensive overview of the data processing steps required to generate valid ML inputs, including molecular representation, augmentation, tokenization, embedding, and spectral deconvolution. We examine the advantages and disadvantages of different modeling strategies and summarize existing models from forensic toxicology and related domains. Applications are grouped into spectra-to-compound, compound-to-spectra, and classification models, with attention to recent advances and the practical challenges of limited data, polysubstance use, and validation. By leveraging advances from related fields, ML can enhance forensic HRMS workflows, enabling more efficient unknown screening, structural elucidation, and classification of emerging substances. This review aims to bridge disciplinary perspectives and support the practical integration of ML into routine forensic toxicology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".