{"slug": "role-aware-morgan-fingerprints-for-reaction-yield-prediction", "title": "Role-Aware Morgan Fingerprints for Reaction Yield Prediction", "summary": "Researchers proposed MFP, a reaction yield prediction method built on role-aware Morgan fingerprints that aggregates count-based circular fingerprints by chemical role (reactant, reagent, product) and combines them with transformation-sensitive difference features into a fixed-length descriptor fed to a feed-forward neural regressor. Tested against YieldBERT (with and without data augmentation) and GNAN under a shared preprocessing and evaluation protocol, MFP reached R2 = 0.878 on the Suzuki-Miyaura benchmark and R2 = 0.969 on Buchwald-Hartwig while training an order of magnitude faster than graph- or Transformer-based alternatives. An ablation found radius-2 representations at nBits = 2048 gave the best balance of accuracy, speed, and cross-split stability on both datasets.", "body_md": "arXiv:2609.22167v1 Announce Type: new \nAbstract: Predicting reaction yield from molecular structure and reaction context can cut experimental trial-and-error and speed up condition screening in synthetic chemistry. Recent methods for this task use learned representations such as graph neural networks or Transformer encoders over reaction SMILES (Simplified Molecular Input Line Entry System), but these approaches carry heavy preprocessing overhead and can break when input formatting is inconsistent. We propose MFP, a reaction yield prediction method built on role-aware Morgan fingerprints where count-based circular fingerprints are computed for each reaction component, aggregated by chemical role (reactant, reagent, product), and combined with transformation-sensitive difference features into a fixed-length reaction descriptor fed to a feed-forward neural regressor. We test MFP against state of the art methods such as YieldBERT (with and without data augmentation) and GNAN (graph neural network) on the Suzuki-Miyaura and Buchwald-Hartwig benchmarks using a shared preprocessing and evaluation protocol. MFP reaches R2 = 0.878 on Suzuki-Miyaura and R2 = 0.969 on Buchwald-Hartwig while training an order of magnitude faster than graph- or Transformer-based alternatives. A formal complexity analysis confirms that MFP folds all representation cost into a one-time preprocessing step, removing the per-epoch message-passing overhead that graph methods carry. An ablation over fingerprint radius and folded vector length shows that radius-2 representations at nBits =2048 give the best balance of accuracy, speed, and cross-split stability on both datasets. These results establish MFP as an effective, reproducible, and efficient baseline for reaction yield prediction.", "url": "https://wpnews.pro/news/role-aware-morgan-fingerprints-for-reaction-yield-prediction", "canonical_source": "https://www.machinebrief.com/news/role-aware-morgan-fingerprints-for-reaction-yield-prediction-gqvl", "published_at": "2026-09-22 04:00:00+00:00", "updated_at": "2026-09-22 05:24:26.179970+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "neural-networks"], "entities": ["MFP", "YieldBERT", "GNAN", "Suzuki-Miyaura", "Buchwald-Hartwig", "Morgan fingerprints", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/role-aware-morgan-fingerprints-for-reaction-yield-prediction", "markdown": "https://wpnews.pro/news/role-aware-morgan-fingerprints-for-reaction-yield-prediction.md", "text": "https://wpnews.pro/news/role-aware-morgan-fingerprints-for-reaction-yield-prediction.txt", "jsonld": "https://wpnews.pro/news/role-aware-morgan-fingerprints-for-reaction-yield-prediction.jsonld"}}