Transfer Learning for Heterocycle Retrosynthesis
Bibliographic record
Abstract
Heterocycles are important scaffolds in medicinal chemistry that can be used to modulate the binding mode as well as the pharmacokinetic properties of drugs. The importance of heterocycles has been exemplified by the publication of numerous data sets containing heterocyclic rings and their properties. However, those data sets lack synthetic routes toward the published heterocycles. Consequently, novel and uncommon heterocycles are not easily synthetically accessible. While retrosynthetic prediction models could usually be used to assist synthetic chemists, their performance is poor for heterocycle formation reactions due to low data availability. In this work, we compare the use of four different transfer learning methods to overcome the low data availability problem and improve the performance of retrosynthesis prediction models for ring-breaking disconnections. The mixed fine-tuned model achieves top-1 accuracy of 36.5%, and, moreover, 62.1% of its predictions are chemically valid and ring-breaking. Furthermore, we demonstrate the applicability of the mixed fine-tuned model in drug discovery by recreating synthetic routes toward two drug-like targets published in 2023. Finally, we introduce a method for further fine-tuning the model as new reaction data becomes available.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".