De Novo Design of Nonpeptidic Compounds Targeting the Interactions between Interferon-α and its Cognate Cell Surface Receptor
Bibliographic record
Abstract
Type 1 interferons (IFN) bind specifically to the corresponding receptor, IFNAR. Agonists and antagonists for IFNAR have potential therapeutic value in the treatment of viral infections and systemic lupus erythematosus, respectively. Specific sequences on the surface of IFN, IFN receptor recognition peptides (IRRPs) mediate the binding and signal transduction when IFN interacts with IFNAR. Structural features of two such IRRPs, IRRP-1 and IRRP-3, were used as templates to design small molecule mimetics. In silico screening was used to identify the molecular structural features mimicking their surface characteristics. A set of 26 compounds were synthesized and their ability to interfere with IFN-IFNAR interactions was investigated. Two compounds exhibited antagonist activity, specifically, blocking IFN-inducible Stat phosphorylation Stat complex-DNA binding. Design principles revealed here pave the way toward a novel series of small molecules as antagonists for IFN-IFNAR interactions.
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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".