Bibliographic record
Abstract
Over the last decade, the health science landscape has been occupied by numerous biotech’s and start-up companies. Most of the time, the scientific level of these companies is outstanding, the way new molecular entities have been developed has evolved with no doubt but despite this evolution changes it must be acknowledged that it still has certain limitations as to the way it functions. Drug development should be regarded as a drawer chest where each drawer represents a drug development step, such as preclinical, preformulation, formulation, regulatory affairs and clinical should be opened and closed at the same time. This short communication will put the emphasis on the preclinical and the chemistry manufacturing and controls (CMC) phases since it has been noted that these sections are plus or less neglected in early drug development. This is confirmed by the fact that almost 50% of the new chemical entities are failing during the preclinical phase and the fact that new molecular entities are becoming more and more difficult to formulate (showing bad druggability profile), that has shown without a doubt that early drug development should be tailored around these two early drug development steps
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.004 |
| 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".