| Téléchargement | - Voir le manuscrit accepté : Non-autoregressive electron redistribution modeling for reaction prediction (PDF, 516 Kio)
- Voir les données supplémentaires : Non-autoregressive electron redistribution modeling for reaction prediction (PDF, 846 Kio)
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| Lien | http://proceedings.mlr.press/v139/bi21a.html |
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| Auteur | Rechercher : Bi, Hangrui; Rechercher : Wang, Hengyi; Rechercher : Shi, Chence; Rechercher : Coley, Connor; Rechercher : Tang, Jian; Rechercher : Guo, Hongyu1 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 38th International Conference on Machine Learning, ICML 2021, July 18-24, 2021, Virtual Event |
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| Résumé | Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules. Such autoregressive methods, however, not only require a pre-defined order for the incremental construction but preclude the use of parallel decoding for efficient computation. To address these issues, we devise a non-autoregressive learning paradigm that predicts reaction in one shot. Leveraging the fact that chemical reactions can be described as a redistribution of electrons in molecules, we formulate a reaction as an arbitrary electron flow and predict it with a novel multi-pointer decoding network. Experiments on the USPTO-MIT dataset show that our approach has established a new state-of-the-art top-1 accuracy and achieves at least 27 times inference speedup over the state-of-the-art methods. Also, our predictions are easier for chemists to interpret owing to predicting the electron flows. |
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| Date de publication | 2021 |
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| Maison d’édition | International Conference on Machine Learning |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 1f7952ac-fb93-4e2c-b8a7-098ab9419c50 |
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| Enregistrement créé | 2022-03-29 |
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| Enregistrement modifié | 2022-03-31 |
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