ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning

ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning

Abstract Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and...

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Main Authors: Mingyang Wang, Shuai Li, Jike Wang, Odin Zhang, Hongyan Du, Dejun Jiang, Zhenxing Wu, Yafeng Deng, Yu Kang, Peichen Pan, Dan Li, Xiaorui Wang, Xiaojun Yao, Tingjun Hou, Chang-Yu Hsieh
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-024-54456-y
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https://doi.org/10.1038/s41467-024-54456-y

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