Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides
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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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".