Download | - View accepted manuscript: Non-autoregressive electron redistribution modeling for reaction prediction (PDF, 516 KiB)
- View supplementary information: Non-autoregressive electron redistribution modeling for reaction prediction (PDF, 846 KiB)
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Link | http://proceedings.mlr.press/v139/bi21a.html |
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Author | Search for: Bi, Hangrui; Search for: Wang, Hengyi; Search for: Shi, Chence; Search for: Coley, Connor; Search for: Tang, Jian; Search for: Guo, Hongyu1 |
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Affiliation | - National Research Council of Canada. Digital Technologies
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Format | Text, Article |
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Conference | 38th International Conference on Machine Learning, ICML 2021, July 18-24, 2021, Virtual Event |
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Abstract | 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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Publication date | 2021 |
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Publisher | International Conference on Machine Learning |
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In | |
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Language | English |
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Peer reviewed | Yes |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 1f7952ac-fb93-4e2c-b8a7-098ab9419c50 |
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Record created | 2022-03-29 |
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Record modified | 2022-03-31 |
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