Open-access public-private partnerships to enable drug discovery--new approaches.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The productivity of the pharmaceutical industry, as assessed by the number of NMEs produced per US dollar spent in R&D, has been in steady decline during the past 40 years. This decline in productivity not only poses a significant challenge to the pharmaceutical industry, but also to society because of the importance of developing drugs for the treatment of unmet medical needs. The major challenge in progressing a new drug to the market is the successful completion of clinical trials. However, the failure rate of drugs entering trials has not decreased, despite various technological and scientific breakthroughs in recent decades, and despite intense target validation efforts. This lack of success suggests limitations in the fundamental understanding of target biology and human pharmacology. One contributing factor may be the traditional secrecy of the pharmaceutical sector, a characteristic that does not promote scientific discovery in an optimal manner. Access to broader knowledge relating to target biology and human pharmacology is difficult to obtain because interactions between researchers in industry and academia are typically restricted to closed collaborations in which the knowledge gained is confidential.However, open-access collaborative partnerships are gaining momentum in industry, and are also favored by funding agencies. Such open-access collaborations may be a powerful alternative to closed collaborations; the sharing of early-stage research data is expected to enable scientific discovery by engaging a broader section of the scientific community in the exploration of new findings. Potentially, the sharing of data could contribute to an increased understanding of biological processes and a decrease in the attrition of clinical programs.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 it