Introductory Medicinal Chemistry for Pharmacy Students: An Assignment-Based Online Assessment Strategy
Bibliographic record
Abstract
New assessment approaches for medicinal chemistry in an introductory course within the pharmacy curriculum are presented. A required introductory pharmaceutical sciences course specific for first year entry-to-practice pharmacy (PharmD) students was developed concurrently within the mandated online learning environment of COVID19. Instead of in-person or online examinations for the medicinal chemistry section, students were required to complete online assignments over the semester. The first series of assignments involved interpretation of a series of specific drug-target PDB structures, using molecular viewing software, to devise new drug analogues, and to rationalize the structural modifications based on proposing specific molecular interactions with the target, with structures being submitted to an online portal as SMILES codes. The final assignment required students to create an online 3 min video describing a specific drug–target interaction, the mechanism of action, structure–activity and additional considerations (adsorption, distribution, metabolism, excretion, toxicity) relevant to the specific drug. In subsequent academic years, the same course was delivered in-person to the first year pharmacy students and quantitative feedback collected. Specific questions were posed in addition to those evaluating the instructor, to better understand the student perspective on the assignments. Initial qualitative feedback was highly supportive of the assignment-based assessment strategy. In subsequent years the student feedback was quantified, and the data indicated that the students preferred the assignments over multiple choice or short answer examination assessment.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".