Hypothesis for changing models: current pharmaceutical paradigms, trends and approaches in drug discovery
Bibliographic record
Abstract
Despite the increasing availability of chemicals, the number of New Drug Approvals (NDA) from the Food and Drug Administration (FDA) remains unchanged. The number of chemical structures available online via web-based open source applications will reach the symbolic 1 billion in the 10 next years. However, for no apparent reasons, the number of NDA accepted yearly has not changed in the past 25 years. One of the emerging paradigms of Big Pharma is that the more we know about molecular mechanisms and cell signaling pathways, the less we understand how to use this knowledge to make New Chemical Entities (NCE). Moreover, the annual number of pharmaceutical patents collected in the OCSE database has virtually not increased. Unexpectedly, the number of patents originating in the USA is decreasing significantly, while Asia is doing very well. The comparison between the number of NCEs and the American investment in Research and Development (R&D) in the last 35 years shows that to obtain a new drug blockbuster, the total investment is quasi 4 USD billion. One of the peculiarities is the inverse relationship between the investment in R&D and the continued shortfall in productivity. A main reason for this decline is that the quality of scientific reasoning done by experienced chemists is too often replaced by Big Data . It is time to change the role of chemistry in Big Pharma and to re-position it as the central science to progress and to lead to much needed innovation.
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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".