Probe my Pathway (PmP): a portal to explore the chemical coverage of the human Reactome
Bibliographic record
Abstract
Deciphering pathway-phenotype associations is critical for a system-wide understanding of cells and the chemistry of life. An approach to reach this goal is to systematically modulate pathways pharmacologically. The targeted and controlled regulation of an increasing number of proteins is becoming possible, thanks to the growing list of chemical probes and chemogenomic compounds available to cell biologists, but no resource is available that directly maps these chemical tools on cellular pathways. To fill this gap, we developed Probe my Pathway (PmP), a database where high-quality chemical probes and well-characterized sets of chemogenomic compounds are mapped on all the human pathways of the Reactome database. The web interface allows users to browse the data via icicle charts or search the data for compounds, proteins, or pathways. Chemists can rapidly find pathways with low chemical coverage or explore the structural chemistry of ligands targeting specific cellular machineries. Cell biologists can look for chemical probes targeting different proteins in the same pathway or find which pathways are targeted by chemical probes of interest. PmP is updated annually and will grow with the expanding chemical tool kit produced by Target 2035 and other efforts. Database URL: https://apps.thesgc.org/pmp/.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.029 |
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".