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Record W3176164384 · doi:10.33774/chemrxiv-2021-qc8vv

A general reactivity map for predicting outcomes in palladium-catalyzed cross-coupling

2021· preprint· en· W3176164384 on OpenAlexafffund
Jingru Lu, Sofia Donnecke, Irina Paci, David C. Leitch

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute CanadaUniversity of Victoria
KeywordsSonogashira couplingPalladiumChemistrySteric effectsCatalysisReactivity (psychology)ArylElectrophileAryl halideOxidative additionCombinatorial chemistryComputational chemistryCoupling reactionSelectivityOrganic chemistry

Abstract

fetched live from OpenAlex

Making accurate, quantitative predictions of chemical reactivity based on molecular structure is an unsolved problem in chemical synthesis, particularly for complex molecules. We report a generally applicable and mechanistically based structure-reactivity model for the oxidative addition of (hetero)aryl halides to palladium(0), which is a key step in myriad catalytic processes. This model links simple molecular descriptors – electrostatic potential, steric parameters, and bond strengths – to relative rates of oxidative addition for 71 electrophile substrates. Because oxidative addition often controls the rate and/or selectivity of palladium-catalyzed cross-coupling reactions, this model can be used to make predictions about catalytic reaction outcomes. Demonstrated applications include a quantitative model for the rate of 410 different Sonogashira coupling reactions, and successful site-selectivity predictions for a series of multihalogenated substrates relevant to the synthesis of pharmaceuticals and natural products.

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)
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 score0.391
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.329
Teacher spread0.299 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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