Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides
Why this work is in the frame
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Bibliographic record
Abstract
Secondary N -arylsulfonamides are common in pharmaceutical compounds owing to their valuable physicochemical properties. Direct N -arylation of primary sulfonamides presents a modular approach to this scaffold but remains a challenging disconnection for transition metal-catalyzed cross coupling broadly, including the Chan–Lam (CL) coupling of nucleophiles with (hetero)aryl boronic acids. Although the CL coupling reaction typically operates under mild conditions, it is also highly substrate-dependent and prone to overarylation, limiting its generality and predictivity. To address these gaps, we employed data science tools in tandem with high-throughput experimentation to study and model the CL N -arylation of primary sulfonamides. To minimize bias in training set design, we applied unsupervised learning to systematically select a diverse set of primary sulfonamides for high-throughput data collection and modeling, resulting in a novel data set of 3,904 reactions. This workflow enabled us to identify broadly applicable, highly selective conditions for the CL coupling of aliphatic and (hetero)aromatic primary sulfonamides with complex organoboron coupling partners. We also generated a regression model that successfully identifies not only high-yielding conditions for the CL coupling of various sulfonamides but also sulfonamide features that dictate reaction outcome.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it