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Record W4400978676 · doi:10.1021/jacs.4c08404

Late-Stage C(<i>sp</i><sup>2</sup>)–C(<i>sp</i><sup>3</sup>) Diversification via Nickel Oxidative Addition Complexes

2024· article· en· W4400978676 on OpenAlexfundno aff
Carlota Odena, Tomás G. Santiago, María Lourdes Linares, Nahury Castellanos‐Blanco, Ryan T. McGuire, Belén Chaves‐Arquero, José Manuel Alonso Segura, A. Dieguez-Vazquez, Eric Tan, Jesús Alcázar, Peter Buijnsters, Santiago Cañellas, Rubén Martı́n

Bibliographic record

VenueJournal of the American Chemical Society · 2024
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaInstitut Català d'Investigació QuímicaAgencia Estatal de InvestigaciónMinisterio de Ciencia, Tecnología e Innovación Productiva
KeywordsChemistryNickelOxidative additionRobustness (evolution)Combinatorial chemistryMoleculeDiversification (marketing strategy)Drug discoveryStereochemistryCatalysisOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Herein, we describe nickel oxidative addition complexes (Ni-OACs) of drug-like molecules as a platform to rapidly generate lead candidates with enhanced C( sp 3 ) fraction. The potential of Ni-OACs to access new chemical space has been assessed not only in C( sp 2 )–C( sp 3 ) couplings but also in additional bond formations without recourse to specialized ligands and with improved generality when compared to Ni-catalyzed reactions. The development of an automated diversification process further illustrates the robustness of Ni-OACs, thus offering a new gateway to expedite the design–make–test–analyze (DMTA) cycle in drug discovery.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.208
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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
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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

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