The Promise and Peril of Chemical Probe Negative Controls
Bibliographic record
Abstract
Chemical probes are selective modulators that are used in cell assays to link a phenotype to a gene and have become indispensable tools to explore gene function and discover therapeutic targets. Chemical probe off-targets are a confounding factor as the observed phenotype may be driven by inhibition of an unknown off-target instead of the targeted protein. A negative control, a close chemical analog of the chemical probe that is inactive against the intended target, is typically used to verify that the phenotype is indeed driven by the targeted protein. Here, we compare the selectivity profiles of four unrelated chemical probes and their respective negative controls. We find that controls that chemically deviate from the probe by a single heavy atom can be inactive against up to 80% of known off-targets if the chemical modification has a charge-neutralizing effect. In such cases, a loss in phenotype upon treatment with the negative control may be driven by loss of inhibition of an off-target. To expand this analysis, we inspect the crystal structures of 90 pairs of unrelated proteins, where both proteins within each pair is in complex with the same drug-like ligand. We computationally estimate that in 50% of cases, methylation of the ligand (a simple chemical modification often used to generate negative controls) at a position that will preclude binding to one protein (the intended target) will also preclude binding to the other (the off-target). These results emphasize the need to select negative controls with care and profile both chemical probes and negative controls against diverse protein arrays to verify that off-targets of probes are also hit by negative controls. When available, a best practice should be to verify that two unrelated chemical probes targeting the same protein elicit the same phenotype.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".