Predicting success in Cu-catalyzed C-N coupling reactions using data science.

ABSTRACT: Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. Thi

SUBMITTER: Samha MH 

PROVIDER: S-EPMC10793951 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature