Combinatorial Assembly of Small Molecules into Bivalent Antagonists of TrkC or TrkA Receptors
Bibliographic record
Abstract
A library of peptidomimetics was assembled combinatorially into dimers on a triazine-based core. The pharmacophore corresponds to β-turns of the neurotrophin polypeptides neurotrophin-3 (NT-3), nerve growth factor (NGF), or brain-derived neurotrophic factor (BDNF). These are the natural ligands for TrkC, TrkA, and TrkB receptors, respectively. The linker length and the side-chain orientation of each monomer within the bivalent mimics were systematically altered, and the impact of these changes on the function of each ligand was evaluated. While the monovalent peptidomimetics had no detectable binding or bioactivity, four bivalent peptidomimetics (2c, 2d, 2e, 3f) are selective TrkC ligands with antagonistic activity, and two bivalent peptidomimetics (1a, 1b) are TrkC and TrkA ligands with antagonistic activity. All these bivalent compounds block ligand-dependent receptor activation and cell survival, without affecting neuritogenic differentiation. This work adds to our understanding of how the neurotrophins function through Trk receptors, and demonstrates that peptidomimetics can be designed to selectively disturb specific biological signals, and may be used as pharmacological probes or as therapeutic leads. The concept of altering side-chain, linker length, and sequence orientation of a subunit within a pharmacophore provides an easy modular approach to generate larger libraries with diversified bioactivity.
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 | 0.000 | 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) | 0.001 | 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".