Defining Drug Targets in Yeast Haploinsufficiency Screens: Application to Human Translational PharmacologyA presentation from the American Society for Pharmacology and Experimental Therapeutics (ASPET) Centennial Meeting at the Experimental Biology 2008 Meeting, San Diego, California, 5 to 9 April 2008.
Bibliographic record
Abstract
A major challenge in drug discovery is to identify the cellular targets responsible for the pharmacological activity of drug candidates. In the yeast Saccharomyces cerevisiae, a heterozygous diploid mutant collection of approximately 6000 strains, in each of which one copy of a single gene is deleted, is commercially available. With this collection, it is possible to evaluate the role of each gene product in the response of cells to a drug. Drug-induced haploinsufficiency refers to the situation where a heterozygous diploid mutant is more sensitive to a drug than is the wild-type strain. Drug-induced haploinsufficiency screening has the potential to reveal pharmacological targets of drugs and those that contribute to undesired side effects, as well as gene products involved in drug transport, metabolism, or resistance. Using published studies, I present advantages and limitations of this technique and discuss its value for predicting drug targets in human cells.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".