Recent Advances in Saturated N-Heterocycle C–H Bond Functionalization for Alkylated N-Heterocycle Synthesis
Bibliographic record
Abstract
Abstract The prominence of saturated N-heterocycle motifs in pharmaceuticals is undeniable. Challenges associated with the alkylation of saturated N-heterocycle scaffolds to efficiently access new drug analogues are hampered by synthetically laborious routes. Stereocontrolled alkyl-substitutions onto saturated N-heterocycles are particularly difficult to access in high yields by traditional synthetic methods. Alternatively, C–H bond functionalization provides a new and powerful synthetic avenue by directly and selectively functionalizing/alkylating/ arylating the abundantly available C–H bonds of saturated N-heterocycles. This review highlights complementary methods for directly activating and functionalizing C–H bonds of saturated N-heterocycles chemo-, regio-, and or stereoselectively to access alkylated products. This synthetic challenge has required catalyst development to access useful N-heterocyclic building blocks or for late-stage functionalization. Early transition metal, late transition metal, photoredox, and electrochemical methods are discussed. The selective functionalization of α, β, and γ C–H bonds to form new C–C, C–N, C–O, and C–B bonds is presented. 1 Introduction 2 Early Transition Metal Catalyzed α-Alkylation 3 Late Transition Metal Catalyzed α-Functionalization 4 Photoredox-Catalyzed α-Functionalization 5 Electrochemical α-Functionalization 6 C–H Functionalization of β and γ C–H Bonds 7 Conclusions/Outlook
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".