A generally applicable quantitative reactivity model for nucleophilic aromatic substitution built from simple descriptors
Bibliographic record
Abstract
We report a multivariate linear regression model able to make accurate predictions for the rate and regioselectivity of nucleophilic aromatic substitution (SNAr) reactions based on the electrophile structure. This model uses a diverse training/test set from experimentally-determined relative SNAr rates between benzyl alcohol and 74 unique electrophiles, including heterocycles with multiple substitution patterns. There is a robust linear relationship between the experimental SNAr free energies of activation and three molecular descriptors that can be obtained computationally: the LUMO energy of the electrophile; the average molecular electrostatic potential (ESP) at the carbon undergoing substitution; and the sum of average ESP values for the ortho and para atoms relative to the reactive center. Despite using only simple descriptors calculated from ground state wavefunctions, this model demonstrates excellent correlation with previously measured SNAr reaction rates, and is able to accurately predict site selectivity for multihalogenated substrates: 91% prediction accuracy across 82 individual examples. The excellent agreement between predicted and experimental outcomes makes this easy-to-implement reactivity model a potentially powerful tool for synthetic planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".