Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine learning (ML) models are increasingly being employed in asymmetric catalysis to predict reaction outcomes and optimize enantioselective processes. Despite the trend of expanding data set sizes to improve model performance, asymmetric catalysis presents unique challenges, including the difficulty of acquiring high-quality experimental data and the often-limited availability of structurally diverse examples. Consequently, rational data set design requires the practitioner to choose whether to collect data that maximizes diversity in the training set or data that maximizes representation around a target prediction. A key challenge in these studies is understanding the role of local reaction space─specifically, how much predictive accuracy is driven by nearest neighbors (structurally and electronically similar data points) and the next-nearest neighbors? This study investigates the predictive power of ML models trained with varying levels of local representation in the reaction space. We provide a framework, a radius-based random forest (RaRF) algorithm, to systematically probe the effects of including diverse reactions dissimilar to a target prediction. We show that when the training set is representative of the target reaction, the gains from increasing data set diversity are modest─typically less than 0.1 kcal/mol in predictive error─and increasing to only 0.5 kcal/mol for extrapolative tests, highlighting the need for targeted data set design. Furthermore, these findings hold even for complex architectures and features. Finally, we demonstrate that a targeted, neighborhood-oriented strategy greatly accelerates the identification of predictive models compared to diversity-driven approaches.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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