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Record W2256618014 · doi:10.1089/jir.2015.0123

Small Molecule Agonists for the Type I Interferon Receptor: An <i>In Silico</i> Approach

2015· article· en· W2256618014 on OpenAlexafffund
Lianhu Wei, Angélica M. Bello, Beata Majchrzak-Kita, Noruê Salum, Melissa Lewis, Lakshmi P. Kotra, Eleanor N. Fish

Bibliographic record

VenueJournal of Interferon & Cytokine Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersCiência sem FronteirasCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsIn silicoPotencyComputational biologySmall moleculeRecombinant DNAReceptorPharmacologyInterferonBiological activityBinding siteChemistryCombinatorial chemistryBiologyBiochemistryIn vitroVirologyGene

Abstract

fetched live from OpenAlex

Type I interferons (IFNs) exhibit broad-spectrum antiviral activity, with potential utility against emerging acute virus infections that pose a threat to global health. Recombinant IFN-αs that have been approved for clinical use require cold storage and are administered through intramuscular or subcutaneous injection, features that are problematic for global distribution, storage, and administration. Cognizant that the biological potency of an IFN-α subtype is determined by its binding affinity to the type I IFN receptor, IFNAR, we identified a panel of small molecule nonpeptide compounds using an in silico screening strategy that incorporated specific structural features of amino acids in the receptor-binding domains of the most potent IFN-α, IFN alfacon-1. Hit compounds were selected based on ease of synthesis and formulation properties. In preliminary biological assays, we provide evidence that these compounds exhibit antiviral activity. This proof-of-concept study validates the strategy of in silico design and development for IFN mimetics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.283
GPT teacher head0.423
Teacher spread0.140 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes2
Has abstractyes

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