A conversation on using chemical probes to study protein function in cells and organisms
Bibliographic record
Abstract
Chemical probes are selective small-molecule modulators, usually inhibitors, of their target protein’s function, that can be used in cell or even animal studies to interrogate the functions of their target proteins. Cheryl Arrowsmith, the leader of a new initiative called Target 2035, which seeks to identify a pharmacological modulator for most human proteins by the year 2035, and Paul Workman, the Executive Director of the nonprofit Chemical Probes Portal, an online resource dedicated to chemical probes, talked to Nature Communications about chemical probes, their respective paths to leadership positions in the field, the online resources available to those interested in the topic and the promise and value of open — collaborative — science. The below material is a modified transcript of a long discussion, preserving the conversational tone, but streamlined and edited for clarity, and thus we do not attribute the particular parts to Cheryl or Paul specifically except for when they shared their personal experiences.
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 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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.009 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.032 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".