High-content screening for the discovery of pharmacological compounds: advantages, challenges and potential benefits of recent technological developments
Bibliographic record
Abstract
Emmanuelle Soleilhac PhDa Researcher, Robert Nadon PhDb Associate Professor & Laurence Lafanechere PhD*ac Researchera CEA-Grenoble, Institut de Recherche en Technologies et Sciences pour le Vivant, Centre de Criblage pour des Molécules Bioactives, Département des Sciences du Vivant, 17 rue des Martyrs, Grenoble Cedex 9, 38054, Franceb McGill University, Genome Quebec Innovation Centre, Department of Human Genetics, 740 Avenue du Docteur Penfield, Montreal, Quebec H3A 1A4, Canadac 3CNRS, CEA-Grenoble, Institut de Recherche en Technologies et Sciences pour le Vivant, Département des Sciences du Vivant, Laboratoire de Physiologie Cellulaire Végétale, 17 rue des Martyrs, Grenoble Cedex 9, F-38054, France +33 438 78 66 71; +33 438 78 50 32; † Author for correspondenceImportance 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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".