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Record W3158643576 · doi:10.3762/bjoc.17.76

Beyond ribose and phosphate: Selected nucleic acid modifications for structure–function investigations and therapeutic applications

2021· review· en· W3158643576 on OpenAlexafffund
Christopher Liczner, Kieran Duke, Gabrielle Juneau, Martin Egli, Christopher J. Wilds

Bibliographic record

VenueBeilstein Journal of Organic Chemistry · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsConcordia University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Environmental Health SciencesNational Institutes of HealthNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaAlnylam Pharmaceuticals
KeywordsOligonucleotideChemistryNucleic acidNucleaseGene silencingComputational biologyFunction (biology)Small moleculeRiboseCombinatorial chemistryDNABiochemistryGeneCell biologyBiologyEnzyme

Abstract

fetched live from OpenAlex

Over the past 25 years, the acceleration of achievements in the development of oligonucleotide-based therapeutics has resulted in numerous new drugs making it to the market for the treatment of various diseases. Oligonucleotides with alterations to their scaffold, prepared with modified nucleosides and solid-phase synthesis, have yielded molecules with interesting biophysical properties that bind to their targets and are tolerated by the cellular machinery to elicit a therapeutic outcome. Structural techniques, such as crystallography, have provided insights to rationalize numerous properties including binding affinity, nuclease stability, and trends observed in the gene silencing. In this review, we discuss the chemistry, biophysical, and structural properties of a number of chemically modified oligonucleotides that have been explored for gene silencing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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.

Study designBench or experimental
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

Citations32
Published2021
Admission routes2
Has abstractyes

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