Semi-Synthesis of a Biologically Inspired Library of Natural Product Mimics, from the Lipophilic Marine Natural Product β-Gorgonene
Bibliographic record
Abstract
Inspired by ilimaquinone and other biologically active sesquiterpenes, we have transformed a highly lipophilic, non-drug-like, marine natural product (gorgonene), available as a by-product from the purification of pseudopterosins, into a library of more drug-like derivatives. Our strategy utilizes semi-synthesis to attach suitable structures such as substituted aromatics and heteroaromatics to the gorgonene scaffold to increase its drug-likeness and mimic natural product structures in biological systems. Several synthetic techniques are being explored to incorporate these interesting substructures in a minimum number of steps. The first strategy focuses on skeletal rearrangements and 2+3 cycloaddition. The second involves ozonolysis of gorgonene followed by introduction of structures using carbonyl chemistry. Biotransformation is being used as a third strategy to selectively oxidize synthetically inaccessible carbons on the saturated backbone to facilitate further transformations. The PTP1B (potential therapeutic target for treatment of type 2 diabetes) and cytotoxic activity of the synthesized compounds will be discussed as well as some unexpected and interesting reactivity we have observed in the 1,5-diene system present in our starting scaffold, β-gorgonene.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".