| Téléchargement | - Voir la version finale : A graph to graphs framework for retrosynthesis prediction (PDF, 677 Kio)
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| Lien | http://proceedings.mlr.press/v119/shi20d.html |
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| Auteur | Rechercher : Shi, Chence; Rechercher : Xu, Minkai; Rechercher : Guo, Hongyu1; Rechercher : Zhang, Ming; Rechercher : Tang, Jian |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | International Conference on Machine Learning (ICML 2020), July 12-18, 2020, Virtual |
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| Résumé | A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, and then translates the synthons to the final reactant graphs via a variational graph translation framework. Experimental results show that G2Gs significantly outperforms existing template-free approaches by up to 63% in terms of the top-1 accuracy and achieves a performance close to that of state-of-the-art template-based approaches, but does not require domain knowledge and is much more scalable. |
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| Date de publication | 2020-03 |
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| Maison d’édition | PMLR |
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| Licence | |
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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 | 76aabdda-5642-4f11-bb4e-74a65d1aa464 |
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| Enregistrement créé | 2021-07-28 |
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| Enregistrement modifié | 2021-07-29 |
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