Glycodendrimers: endlessly evolving architecture design, synthetic strategies, and biological applications
Bibliographic record
Abstract
The synthesis of dendritic architectures has steadily advanced to overcome challenges associated with biological applications. Key issues—including reduced toxicity, enhanced target specificity, improved stability, and efficient delivery of drugs, vaccines, and genes—have posed significant hurdles, particularly when scalability for therapeutic translation is required. Among these, optimizing dendrimer systems to engage in carbohydrate-related biological functions has remained a central focus since the introduction of glycodendrimers. The emergence of glycodendrimers has proven especially effective in contexts demanding multivalent binding interactions. This review highlights advancements in the design of multivalent carbohydrate-based nanomaterials, tracing their evolution from the early development of glycopolymers to the emergence of glycodendrimers. It presents key developments in the use of scaffolds featuring an expanded array of surface functional groups alongside variations in the chemical building blocks traditionally employed in classical dendrimer synthesis—culminating in the so-called “onion-peel strategy”. These innovations are further complemented by the integration of orthogonal ligation techniques. Additionally, efforts to streamline the synthesis of multivalent glycoarchitectures have led to the emergence of simplified methodologies, including transition-metal-templated assembly and the self-organization of glycodendrimersomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".