Cationic Amphiphiles, a New Generation of Antimicrobials Inspired by the Natural Antimicrobial Peptide Scaffold
Bibliographic record
Abstract
Naturally occurring cationic antimicrobial peptides (AMPs) and their mimics form a diverse class of antibacterial agents currently validated in preclinical and clinical settings for the treatment of infections caused by antimicrobial-resistant bacteria. Numerous studies with linear, cyclic, and diastereomeric AMPs have strongly supported the hypothesis that their physicochemical properties, rather than any specific amino acid sequence, are responsible for their microbiological activities. It is generally believed that the amphiphilic topology is essential for insertion into and disruption of the cytoplasmic membrane. In particular, the ability to rapidly kill bacteria and the relative difficulty with which bacteria develop resistance make AMPs and their mimics attractive targets for drug development. However, the therapeutic use of naturally occurring AMPs is hampered by the high manufacturing costs, poor pharmacokinetic properties, and low bacteriological efficacy in animal models. In order to overcome these problems, a variety of novel and structurally diverse cationic amphiphiles that mimic the amphiphilic topology of AMPs have recently appeared. Many of these compounds exhibit superior pharmacokinetic properties and reduced in vitro toxicity while retaining potent antibacterial activity against resistant and nonresistant bacteria. In summary, cationic amphiphiles promise to provide a new and rich source of diverse antibacterial lead structures in the years to come.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".