Design of Phenotypic Screens for Bioactive Chemicals and Identification of their Targets by Genetic and Proteomic Approaches
Bibliographic record
Abstract
Cell-based screening using phenotypic assays is a useful means of identifying bioactive chemicals for use as tools to elucidate complex cellular processes. However, the chemicals must display sufficient selectivity and their targets have to be identified. We describe how cell-based screening assays can be designed to maximize the likelihood of discovering selective compounds through the choice of positive readouts, low chemical concentrations and long incubation periods. Examining the potency, efficacy and activity range of chemicals can further help set apart those likely to act more specifically. Identifying the cellular targets of active chemicals can be especially demanding. Secondary screens and the cautious use of the candidate approach can help narrow down their mechanisms of action, but biased approaches may lead to the identification of secondary or even irrelevant targets. We discuss strategies for unbiased target identification by sampling potential targets at the genome-wide and proteome-wide levels. Keywords: Activity-based protein profiling, activity range, Caenorhabditis elegans, candidate approach, chemical biology, chemical-genetic interactions, drug discovery, drug-induced haploinsufficiency, drug targets, proteome profiling
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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".