ChemiRise：数据驱动的逆合成引擎
ChemiRise: a data-driven retrosynthesis engine
摘要：
我们开发了一个名为ChemiRise的端到端逆合成系统，可以快速可靠地为有机化合物提供完整的逆合成路线。该系统在处理过的300多万个有机反应的专利数据库上进行了训练。实验反应进行原子映射，聚类，并提取到反应模板。然后，我们使用模板嵌入训练了一个基于图卷积神经网络的一步反应提议器，并在化合物的有向无环图（DAG）上开发了一个指导算法，以找到最佳候选者进行探索。原子映射算法和一步反应提议者对以前的研究进行了基准测试，并显示出更好的结果。最终产品通过由人类专家审查和评级的逆合成路线进行了演示，显示出令人满意的功能和在现实生活用例中潜在的生产力提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
