Pharmaceutical Applications of Molecular Tweezers, Clefts and Clips
Bibliographic record
Abstract
Synthetic acyclic receptors, composed of two arms connected with a spacer enabling molecular recognition, have been intensively explored in host-guest chemistry in the past decades. They fall into the categories of molecular tweezers, clefts and clips, depending on the geometry allowing the recognition of various guests. The advances in synthesis and mechanistic studies have pushed them forward to pharmaceutical applications, such as neurodegenerative disorders, infectious diseases, cancer, cardiovascular disease, diabetes, etc. In this review, we provide a summary of the synthetic molecular tweezers, clefts and clips that have been reported for pharmaceutical applications. Their structures, mechanism of action as well as in vitro and in vivo results are described. Such receptors were found to selectively bind biological guests, namely, nucleic acids, sugars, amino acids and proteins enabling their use as biosensors or therapeutics. Particularly interesting are dynamic molecular tweezers which are capable of controlled motion in response to an external stimulus. They proved their utility as imaging agents or in the design of controlled release systems. Despite some issues, such as stability, cytotoxicity or biocompatibility that still need to be addressed, it is obvious that molecular tweezers, clefts and clips are promising candidates for several incurable diseases as therapeutic agents, diagnostic or delivery tools.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".