Part 1. Modular Approach to Obtaining Diverse Tetrahydroquinoline-Derived Polycyclic Skeletons for Use in High-Throughput Generation of Natural-Product-like Chemical Probes
Bibliographic record
Abstract
A practical synthesis of a tetrahydroaminoquinoline scaffold (12) was developed that used a stereocontrolled aza Michael as the key reaction. Three tetrahydroquinoline alkaloid-like, tricyclic derivatives 16, 18, and 19 with different medium to macrocyclic ring skeletons were obtained, using this scaffold as the starting material, in a modular manner. The macrocyclic compounds with an isolated olefin and an electron-deficient olefin were obtained by ring-closing metathesis approaches. Compounds 16 and 18 are unique and contain bridged 10- and 12-membered functionalized rings. The NMR studies of these compounds revealed interesting information on the conformation of the bicyclic scaffolds that was dependent on the nature and the size of the macrocyclic rings. Finally, this modular methodology, using compound 21 anchored onto the solid support, successfully led to the generation of different macrocyclic derivatives, 23, 25, and 27 in solid-phase synthesis. The solid-phase synthesis approach outlined in this article has the potential to generate tetrahydroquinoline-based tricyclic compounds containing different medium to macrocyclic architectures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".