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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 07:45 PM -- 07:50 PM (PDT) @
in Applications (Bio) 2 »

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.

Keywords: Computational Biology and Bioinformatics

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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