Incorporating Stories of Sedatives, Spoiled Sweet Clover Hay, and Plants from the Amazon Rainforest into a Pharmaceutical Chemistry Course To Engage Students and Introduce Drug Design Strategies
Bibliographic record
Abstract
This article describes three historical cases of drug discovery and how they were adapted as examples to teach chemical analysis to students pursuing a pharmacy (UK MPharm) and pharmaceutical sciences (BSc Pharmaceutical Sciences) degree. The selected cases were the synthesis of benzodiazepines and the discovery of warfarin and neuromuscular blocking agents. These examples present some peculiarities as they were developed in special circumstances and without the assistance of modern chemical analysis techniques. By incorporating these examples in a pharmaceutical chemistry class, the students became aware of the importance of chemical knowledge in overcoming technical limitations. Moreover, the examples were designed to stimulate the interest of the students in the subject. Three case studies including drug discovery examples were implemented in a chemistry module delivered to pharmacy students. The views of the students (48 MPharm and 7 BSc pharmaceutical sciences) about these lectures was obtained by using a questionnaire. After delivering the lectures, the majority of the students (64%) thought that understanding the history behind some scientific discoveries was important for them. Additionally, they considered that the selected historical examples were not only interesting but useful to understand the material delivered in the pharmaceutical chemistry module (75% of the students).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".