From Wood to Tetrahydro-2-benzazepines in Three Waste-Free Steps: Modular Synthesis of Biologically Active Lignin-Derived Scaffolds
Bibliographic record
Abstract
Inherently complex, lignin-derived aromatic monomers comprising valuable structural moieties present in many pharmaceuticals would serve as ideal substrates for the construction of biologically active molecules. Here, we describe a strategy that incorporates all intrinsic functional groups present in platform chemicals obtained by lignin depolymerization into value-added amines, using sustainable catalytic methods and benign solvents. Our strikingly efficient protocol provides access to libraries of aminoalkyl-phenol derivatives and seven-membered N-heterocycles directly from wood in two, respectively three, waste-free steps. Several molecules in these libraries have shown promising antibacterial or anticancer activities, emphasizing the advantage of this modular synthetic strategy and the potential for drug discovery. The sustainable catalytic pathways presented here can lead to significant benefits for the pharmaceutical industry where reduction of hazardous waste is a prime concern, and the described strategies that lead to high-value products from non-edible biomass waste streams also markedly increase the economic feasibility of lignocellulosic biorefineries.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".