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Record W4406642051 · doi:10.1016/j.xinn.2025.100809

Oxidant-free cross-dehydrogenative oxyalkylation enables late-stage functionalization of drugs

2025· article· en· W4406642051 on OpenAlexaff
Jiahao Li, Jianbin Li, Chung-Cheng Wu, Zhiyu Tu, Bo‐Shuai Mu, Yang Xu, Longlong Song, Mengxin Xu, Xi‐Yang Cui, Chao‐Jun Li, Zhibo Liu

Bibliographic record

VenueThe Innovation · 2025
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsMcGill University
FundersBeijing Municipal Natural Science FoundationPeking UniversityMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaChangping Laboratory
KeywordsSurface modificationStage (stratigraphy)ChemistryBiology

Abstract

fetched live from OpenAlex

Late-stage functionalization is an attractive strategy that allows chemists to bypass lengthy synthetic processes, facilitating the rapid generation of drug analogs with potentially enhanced pharmacokinetic and pharmacological properties. This study describes a novel approach for cross-dehydrogenative oxyalkylation, leveraging a unique γ-ray-enabled photoredox process to generate oxyalkyl radicals, followed by a Minisci-type addition in an aqueous solution. The metal- and oxidant-free aqueous conditions, coupled with excellent functional group compatibility, establish this method as a versatile protocol for the late-stage oxyalkylation of unprotected, structurally complex drug molecules. Notably, this method demonstrated improved pharmacokinetics in hydroxymethylated fibroblast activation protein inhibitor (FAPI) molecules, highlighting its potential to accelerate drug discovery 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.312
Teacher spread0.289 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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