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Record W2124388656 · doi:10.2217/fvl.11.35

HIV-1 Reverse Transcriptase Inhibitors: Beyond Classic Nucleosides and Non-Nucleosides

2011· article· en· W2124388656 on OpenAlexaff
Brian J. Scarth, Maryam Ehteshami, Greg L. Beilhartz, Matthias Götte

Bibliographic record

VenueFuture Virology · 2011
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsReverse transcriptaseNucleosideNucleoside analogueRNase HAllosteric regulationEnzymeNucleotideHuman immunodeficiency virus (HIV)ChemistryNucleoside Reverse Transcriptase InhibitorRNase PDrugPharmacologyVirologyBiochemistryBiologyRNA

Abstract

fetched live from OpenAlex

Reverse transcriptase (RT) of HIV-1 remains an important target in current treatments of HIV-1 infection. Clinically available inhibitors of HIV-1 RT include nucleoside analog RT inhibitors and non-nucleoside RT inhibitors. Nucleoside analog RT inhibitors compete with the natural dNTP substrate and act as chain terminators, while non-nucleoside RT inhibitors bind to an allosteric pocket, inhibiting polymerization noncompetitively. In addition to these two classes of approved drugs, there are a number of RT inhibitors that target the enzyme in different ways. These include nonobligate chain terminators, nucleotide-competing RT inhibitors, pyrophosphate analogs and compounds that inhibit the RT-associated RNase H activity. Here, we review the mechanisms of action associated with these compounds and discuss opportunities and challenges in drug discovery and development efforts.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2011
Admission routes1
Has abstractyes

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