Synthesizing Novel Anthraquinone Natural Product-like Compounds To Investigate Protein–Ligand Interactions in Both an in Vitro and in Vivo Assay: An Integrated Research-Based Third-Year Chemical Biology Laboratory Course
Bibliographic record
Abstract
A new undergraduate program in chemical biology was launched in 2008 to provide a unique learning experience for those students interested in this interdisciplinary science. An innovative undergraduate chemical biology laboratory course at the third-year level was developed as a key component of the curriculum. The laboratory course introduces students to some of the fundamental aspects of chemical biology through a series of integrated, research-based laboratory experiments, which illustrate how different areas of the discipline are interconnected. The 12-week stand-alone laboratory course consists of a weekly 1-h lecture and a 4-h laboratory period. During the course, students synthesize a novel anthraquinone natural product-like compound, purify a protein, and perform both an in vitro and an in vivo biological assay to investigate protein–ligand interactions with these materials. Two iterations of the course have been completed with class sizes of 21 (2010) and 11 (2011) students. Because of the modular nature of the course (i.e., organic, biological, and analytical sections), it is widely applicable to a range of upper-level chemistry, biology, molecular biology, and biochemistry undergraduate courses.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".