Late-Stage C(sp2)–C(sp3) Diversification via Nickel Oxidative Addition Complexes
Bibliographic record
Abstract
Nickel catalysis has emerged as a powerful technique for streamlining the access to exceedingly complex organic molecules from simple precursors. However, nickel-catalyzed cross-couplings with advanced synthetic intermediates still remain a considerable challenge. Herein, we describe a technique based on the utilization of nickel oxidative addition complexes (Ni-OAC) of drug-like molecules as a platform to rapidly and reliably generate lead candidates with enhanced C(sp3) fraction. The potential of Ni-OACs to access new chemical space has been assessed in three different C(sp2)–C(sp3) bond-forming events without recourse to specialized ligand backbones. Reactions with Ni-OACs proceed under exceptionally mild conditions and with improved generality when compared to nickel-catalyzed reactions. The development of an automated process for forging C(sp2)–C(sp3) architectures further illustrates the robustness and generality of Ni-OACs, thus offering a new gateway to expedite the design-make-test-analyze (DMTA) cycle in drug discovery
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".