Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction

• URL: http://arxiv.org/abs/2106.07801v1
• Date: Tue, 8 Jun 2021 16:39:08 GMT
• Title: Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
• Authors: Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo
• Abstract summary: We devise a non-autoregressive learning paradigm that predicts reaction in one shot. 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.
• Score: 26.007965383304864
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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