Small Molecules, Big Players: the National Cancer Institute's Initiative for Chemical Genetics
Bibliographic record
Abstract
In 2002, the National Cancer Institute created the Initiative for Chemical Genetics (ICG), to enable public research using small molecules to accelerate the discovery of cancer-relevant small-molecule probes. The ICG is a public-access research facility consisting of a tightly integrated team of synthetic and analytical chemists, assay developers, high-throughput screening and automation engineers, computational scientists, and software developers. The ICG seeks to facilitate the cross-fertilization of synthetic chemistry and cancer biology by creating a research environment in which new scientific collaborations are possible. To date, the ICG has interacted with 76 biology laboratories from 39 institutions and more than a dozen organic synthetic chemistry laboratories around the country and in Canada. All chemistry and screening data are deposited into the ChemBank web site (http://chembank.broad.harvard.edu/) and are available to the entire research community within a year of generation. ChemBank is both a data repository and a data analysis environment, facilitating the exploration of chemical and biological information across many different assays and small molecules. This report outlines how the ICG functions, how researchers can take advantage of its screening, chemistry and informatic capabilities, and provides a brief summary of some of the many important research findings.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".