Kibichii Bore, Ravi Kiran Raman, et al.
ICBC 2019
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.
Kibichii Bore, Ravi Kiran Raman, et al.
ICBC 2019
Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, et al.
SC 2021
Bo Zhao, Iordan Ganev, et al.
ICLR 2023
Brian Quanz, Pavithra Harsha, et al.
INFORMS 2022