Extolling the benefits of molecular therapeutic lipidation
Bibliographic record
Abstract
The conjugation of drug or molecular recognition motif to a hydrophobic fatty entity, for purpose of drug-membrane localization, has been a molecular strategy utilized for targeted inhibition of pathways involved in diseased cells. In general, membrane-anchored inhibitor structures have been composed of either a lipid or sterol group coupled via a broad range of inert linkers to either a peptide or small molecule protein recognition agent. Whilst not adhering to the molecular paradigms of modern medicinal chemistry, this approach has afforded peptidic-based therapeutics with improved cellular and in vivo efficacy, leading to more selective targeting of membrane associated protein targets and the effective immobilization of cytosolic signaling proteins through membrane anchorage. The evidence suggests that membrane-anchored peptidic inhibitors are more selective, potent, structurally rigid, and possess enhanced cell permeability profiles as compared to their non-lipidated precursors. This perspectives article will review the application of lipid or sterol conjugation to peptide inhibitors (lipo-molecules) to circumvent the poor cell permeability and metabolic labilities associated with peptidic therapeutics. In addition, the concept of protein-membrane anchorage as a novel drug modality for inhibiting cytosolic signaling protein motility in cells will be reviewed and its merits as an approach to inhibiting protein complexation, protein nuclear translocation and their potential for more effective targeting of membrane associated targets.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".