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Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Hangrui Bi · Hengyi Wang · Chence Shi · Connor Coley · Jian Tang · Hongyu Guo

Wed Jul 21 09:00 PM -- 11:00 PM (PDT) @ Virtual

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.

Author Information

Hangrui Bi (Peking University)
Hengyi Wang (Peking University)
Chence Shi (University of Montreal)
Connor Coley (MIT)
Jian Tang (HEC Montreal & MILA)
Hongyu Guo (National Research Council Canada)

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