High-content screening for the discovery of pharmacological compounds: advantages, challenges and potential benefits of recent technological developments.
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Screening compounds with cell-based assays and microscopy image-based analysis is an approach currently favored for drug discovery. Because of its high information yield, the strategy is called high-content screening (HCS). AREAS COVERED IN THIS REVIEW: This review covers the application of HCS in drug discovery and also in basic research of potential new pathways that can be targeted for treatment of pathophysiological diseases. HCS faces several challenges, however, including the extraction of pertinent information from the massive amount of data generated from images. Several proposed approaches to HCS data acquisition and analysis are reviewed. WHAT THE READER WILL GAIN: Different solutions from the fields of mathematics, bioinformatics and biotechnology are presented. Potential applications and limits of these recent technical developments are also discussed. TAKE HOME MESSAGE: HCS is a multidisciplinary and multistep approach for understanding the effects of compounds on biological processes at the cellular level. Reliable results depend on the quality of the overall process and require strong interdisciplinary collaborations.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".