Which Small Molecule? Selecting Chemical Probes for Use in Cancer Research and Target Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Small-molecule chemical "probes" complement the use of molecular biology techniques to explore, validate, and generate hypotheses on the function of proteins in diseases such as cancer. Unfortunately, the poor selection and use of small-molecule reagents can lead to incorrect conclusions. Here, we illustrate examples of poor chemical tools and suggest best practices for the selection, validation, and use of high-quality chemical probes in cancer research. We also note the complexity associated with tools for novel drug modalities, exemplified by protein degraders, and provide advice and resources to facilitate the independent identification of appropriate small-molecule probes by researchers. SIGNIFICANCE: Validation of biological targets and pathways will be aided by a shared understanding of the criteria of potency, selectivity, and target engagement associated with small-molecule reagents ("chemical probes") that enable that work. Interdisciplinary collaboration between cancer biologists, medicinal chemists, and chemical biologists and the awareness of available resources will reduce misleading data generation and interpretation, strengthen data robustness, and improve productivity in academic and industrial research.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it