RNAi and the drug discovery process
Bibliographic record
Abstract
Introduction Drug discovery is a complex process that seeks to identify therapeutics for treating human disease. It has very high failure rate, and by one estimate, the total cost for a therapeutic successfully brought to market is 803 million dollars (DiMasi et al., 2003). Failure occurs at all points in the process, with failures at the pre-development stages being the most common, and failures at the clinical stages being the most costly. Scientists working in drug discovery are continually challenged to identify ways to improve the process. Current efforts are largely “target-based” approaches. Once chosen, the target may be studied in vitro for more than a year, and in model systems of the disease for up to four years. Errors in determining whether a given target is truly effective in treating a disease may not be detected until Phase II or Phase III clinical studies, which follow many years of study and a financial investment of tens or hundreds of millions of dollars. As such, target validation is a critical aspect of the drug discovery process. The pharmaceutical industry has invested heavily in genomics because of its promise to provide a continuing supply of drug targets (Wiley, 1998; Ohlstein et al., 2000; Baba, 2001). Implicit in this investment has been the expectation that the targets provided by genomics are highly validated (Debouck and Metcalf, 2000). Thus, genomics has grown broadly across the drug discovery process, from target identification to phamacogenomics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".