Evaluation of Virtual Screening as a Tool for Chemical Genetic Applications
Bibliographic record
Abstract
A collection of over 50,000 functionally annotated drugs, clinical candidates, and endogenous ligands was docked in silico against nine binding sites from seven protein targets, representing diverse function and structure, namely the sulfotransferases SULT1E1 and SULT1A3, the histone methyltransferase EHMT1, the histone acetyltransferase MYST3, and the nuclear hormone receptors ERalpha, PPARgamma, and TRbeta. For 5 of the 9 virtual screens, compounds that docked best to the receptors clearly recapitulated known biological functions of the genes or identified novel biology subsequently validated in a separate experimental study. In two cases, the hit list indicated some relevant but isolated biological functions which would probably have been ignored a priori, and selected compounds were completely unrelated to gene function for the last two virtual screens. This study demonstrates that virtual screening of pharmacologically annotated compound libraries can be used to derive target biology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".