Deep Annotation of Donated Chemical Probes (DCP) in Organotypic Human Liver Cultures and Patient-Derived Organoids from Tumor and Normal Colorectum
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Well-characterized small molecules are essential tools for studying the biology and therapeutic relevance of a target protein. However, many compounds reported in the literature and routinely studied in biomedical research lack the potency and selectivity required for mechanistic cellular studies on the function of a given protein. Furthermore, commercially available compounds often do not include useful tools developed by industry as part of their research and development efforts, as they frequently remain proprietary. The freely available donated chemical probe (DCP) library, fueled by generous donations of compounds from industry and academia, enables easy access to a steadily growing collection of these valuable and well-characterized tools. Here, we provide a systematic description of the current DCP library collection and their associated comprehensive characterization data, including a variety of in vitro and cellular assays. Of note, we characterized the set in relevant human primary models by employing hepatotoxicity screening in primary human liver spheroids and viability screening in patient-derived colorectal cancer organoids and matched normal-adjacent epithelium. Taken together, the DCP library represents a well-annotated, openly available collection of tool compounds for studying a wide range of targets, including kinases, G-protein-coupled receptors, and ion channels. As such, it represents a unique resource for the biomedical research community.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".