Pawan Chowdhary, Taiga Nakamura, et al.
INFORMS 2020
Computer-aided synthesis design, automation, and analytics assisted by machine learning are promising resources in the researcher’s toolkit. Each component may alleviate the chemist from routine tasks, provide valuable insights from data, and enable more informed experimental design. Herein, we highlight selected works in the field and discuss the different approaches and the problems to which they may apply. We emphasize that there are currently few tools with a low barrier of entry for non-experts, which may limit widespread integration into the researcher’s workflow.
Pawan Chowdhary, Taiga Nakamura, et al.
INFORMS 2020
Wang Zhou, Levente Klein, et al.
INFORMS 2020
Michael Feffer, Martin Hirzel, et al.
ICML 2022
Weixin Liang, Girmaw Abebe Tadesse, et al.
Nature Machine Intelligence