Open resources for chemical probes and their implications for future drug discovery
Bibliographic record
Abstract
INTRODUCTION: The rational development of new therapeutics requires a thorough understanding of how aberrant signalling affects cellular homeostasis and causes human disease. Chemical probes are tool compounds with well-defined mechanism-of-action enabling modulation of, for example, domain-specific protein properties in a temporal manner, thereby complementing other target validation methods such as genetic gain- and loss-of-function approaches. AREAS COVERED: In this review, the authors summarize recent advances in chemical probe development for emerging target classes such as solute carriers and ubiquitin-related targets and highlight open resources to inform and facilitate chemical probe discovery as well as tool compound selection for target validation and phenotypic screening. EXPERT OPINION: Chemical probes are powerful tools for drug discovery that have led to fundamental insights into biological processes and have paved the way for the development of first-in-class drugs. Open resources can inform on various aspects of chemical probe development and provide access to data and recommendations on use of chemical probes to catalyse collaborative science and help accelerate drug target identification and validation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".