Empowering research in chemical biology and early drug discovery – an update from the European research infrastructure EU-OPENSCREEN
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
EU-OPENSCREEN is the European research infrastructure consortium for chemical biology and early drug discovery. It provides open access to high-throughput screening, chemoproteomics and spatial MS-based omics platforms and medicinal chemistry groups to support the discovery of new biologically active small molecules that act as starting points for the development of new chemical tool compounds and drugs. Since its inauguration in 2018, the research infrastructure evolved from a blueprint to a fully operational platform. As new trends and technologies have an important impact on modern drug discovery, EU-OPENSCREEN continuously expands and refines its portfolio of technologies and expertise. In this perspective, the key achievements of the past six years and the planned activities over the next years are described. We illustrate how scientists can benefit from EU-OPENSCREEN through gaining access to technology platforms and expertise to unlock the extraordinary potential of their research projects and translate them into novel, impactful and innovative applications.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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