Therapeutic peptides targeting the Ras superfamily
Bibliographic record
Abstract
Abstract The Ras superfamily of small GTPases are master regulators of numerous essential processes within the cell, so that when they malfunction, cancer and many other diseases can result. For example, activating Ras mutations are present in approximately 20% of human cancers. As such, they are key therapeutic targets, yet more than three decades of intensive research efforts have failed to produce effective Ras inhibitors in the clinic. This is, in part, due to their relatively smooth surfaces which are difficult to target through traditional drug discovery methods using small molecules. Peptides offer a solution to this issue as they occupy larger surface areas on their targets and therefore offer exquisite selectivity and affinity. However, their use in the past has been limited to extracellular targets due to delivery issues. Recent advances in peptide macrocyclisation, modifications and delivery methods have ignited increased interest in the use of these highly effective biologics for intracellular targets. This review will cover progress made in the development of peptides targeting small GTPases to treat a wide range of diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".