Bibliographic record
Abstract
Carbohydrate protein interactions are at the front of several biological interactions spanning from cell growth and differentiation, cell signaling, apoptosis, cancer, and microbial infections. Classical medicinal glycochemistry has so far concentrated on designing glycosyl transferase and glycohydrolase inhibitors for which only handful candidates have emerged. Added to the complexity of drug resistances, drugs in development have rapidly witnessed this limitation as well. New approaches are therefore highly encouraged. Amongst these, blocking pathogen adhesions to host tissues as an early preventive mechanism is a foreseeable potentiality. It has the clear advantage that the pathogens are unlikely to mutate their anchoring motifs without upsetting their own binding to host tissues. An added dilemma is that these binding interactions are usually too weak to provide suitable drug candidates. As a consequence, the community has successfully come up with multivalent glycoconjugates having greatly enhanced avidity. An alternative strategy in which both monovalent ligands as well as the multivalent scaffolds undergoing QSAR improvement is thus suggested. This review will highlight recent trends toward the design of multivalent glycodendrimers and their biological applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".