Photocatalyzed Decarboxylative B–C Couplings for the Synthesis of Carboranyl Amino Acids and Peptides
Bibliographic record
Abstract
Boron neutron capture therapy (BNCT) is a promising and selective strategy for treating aggressive and refractory tumors, but its clinical success depends on the development of effective boron delivery agents. These agents must offer high tumor selectivity, structural stability, and sufficient boron content─criteria that current clinical options fail to fully satisfy. Herein, we report a visible-light-driven decarboxylative B-C cross-coupling between boron-functionalized carborane carboxylic acids and dehydroalanine (Dha)-containing peptides, enabling the first synthesis of boron-vertex-substituted carboranyl peptides under mild conditions. This photocatalyzed site-selective Giese involves the reaction of photogenerated boron vertex-centered carboranyl radicals to Dha residues, affording carborane-peptide conjugates in good to high yields and with excellent functional group tolerance. Enantiopure boron-vertex-substituted carboranylalanines were successfully synthesized using chiral Karady-Beckwith Dha derivatives, enabling their incorporation into well-defined complex peptides (comprising 5 and 15 residues) via solid-phase peptide synthesis. The synthetic utility of this platform was further demonstrated through a DNA-compatible click reaction, which enabled the attachment of carborane-bearing motifs to DNA tags. Moreover, B-C coupled carboranylalanines were conjugated to biologically relevant molecules such as nucleic acid aptamers to enhance tumor-targeting properties. Preliminary cellular studies confirmed that aptamer-carborane-amino acid conjugates exhibit efficient tumor cell recognition and uptake. Collectively, this work establishes a versatile and late-stage strategy for the site-selective installation of carborane units onto biomolecules via B-C bond formation, significantly expanding the chemical space of boron-rich peptide architectures and advancing the development of next-generation BNCT agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".