Bibliographic record
Abstract
By paying insufficient attention to the physical, solid-state properties of drug compounds, the pharmaceutical industry has missed opportunities for new drugs and imposed untold costs on itself, according to a group of industry leaders. Drug companies can better carry out their core duty—creating new medicines—by drawing more rigorously on materials science and engineering and translating this fundamental science into pharmaceutical development, they say. To promote this goal, the group launched the M3 conference, named for “Molecules, Materials, Medicines.” The third M3 meeting took place on May 19–22 in Banff, Alberta. The group includes Örn Almarsson and Magali Hickey of Alkermes, Patrick Connelly and Michael (Mick) Hurrey of Vertex Pharmaceuticals, Drazen Ostovic of KO Pharm R&D, Matt Peterson of Amgen, and Elizabeth B. Vadas of InSciTech. For years, they advocated for earlier and more rigorous application of solid-state materials science in pharmaceutical R&D. They argued, both in their own workplaces and ...
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.041 | 0.023 |
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".