تقرير
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
| العنوان: | Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction |
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| المؤلفون: | Bi, Hangrui, Wang, Hengyi, Shi, Chence, Coley, Connor, Tang, Jian, Guo, Hongyu |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Chemical Physics, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2106.07801 |
| رقم الأكسشن: | edsarx.2106.07801 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |