ChemiRise: a data-driven retrosynthesis engine
- URL: http://arxiv.org/abs/2108.04682v1
- Date: Mon, 9 Aug 2021 05:13:14 GMT
- Title: ChemiRise: a data-driven retrosynthesis engine
- Authors: Xiangyan Sun, Ke Liu, Yuquan Lin, Lingjie Wu, Haoming Xing, Minghong
Gao, Ji Liu, Suocheng Tan, Zekun Ni, Qi Han, Junqiu Wu, Jie Fan
- Abstract summary: ChemiRise can propose complete retrosynthesis routes for organic compounds rapidly and reliably.
System was trained on a processed patent database of over 3 million organic reactions.
- Score: 19.52621175562223
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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