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arXiv · 2108.04682

ChemiRise: a data-driven retrosynthesis engine

Xiangyan Sun·Ke Liu·Yuquan Lin·Lingjie Wu·Haoming Xing·Minghong Gao·Ji Liu·Suocheng Tan·Zekun Ni·Qi Han·Junqiu Wu·Jie Fan
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Abstract

We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably. The system was trained on a processed patent database of over 3 million organic reactions. Experimental reactions were atom-mapped, clustered, and extracted into reaction templates. We then trained a graph convolutional neural network-based one-step reaction proposer using template embeddings and developed a guiding algorithm on the directed acyclic graph (DAG) of chemical compounds to find the best candidate to explore. The atom-mapping algorithm and the one-step reaction proposer were benchmarked against previous studies and showed better results. The final product was demonstrated by retrosynthesis routes reviewed and rated by human experts, showing satisfying functionality and a potential productivity boost in real-life use cases.

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Xiangyan Sun, Ke Liu, Yuquan Lin, Lingjie Wu, Haoming Xing, Minghong Gao, Ji Liu, Suocheng Tan, Zekun Ni, Qi Han, Junqiu Wu, Jie Fan. 2021-08-09. ChemiRise: a data-driven retrosynthesis engine. https://arxiv.org/abs/2108.04682

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