2021-07-01-bi21a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bi21a	0	Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction	904	913	904-913	904	false	Bi, Hangrui and Wang, Hengyi and Shi, Chence and Coley, Connor and Tang, Jian and Guo, Hongyu	given family Hangrui Bi given family Hengyi Wang given family Chence Shi given family Connor Coley given family Jian Tang given family Hongyu Guo	2021-07-01		Proceedings of the 38th International Conference on Machine Learning	139	inproceedings	date-parts 2021 7 1	http://proceedings.mlr.press/v139/bi21a/bi21a.pdf	label link Supplementary ZIP http://proceedings.mlr.press/v139/bi21a/bi21a-supp.zip