Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality

N. Ian Rinehart, Andrew F. Zahrt, Scott E. Denmark

Research output: Contribution to journalArticlepeer-review


Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past it is intrinsically limited and inefficient. To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization of any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated with physical organic methods to identify the origins of selectivity.

Original languageEnglish (US)
Pages (from-to)592-597
Number of pages6
Issue number7-8
StatePublished - Aug 2021


  • Catalyst optimization
  • Chemoinformatic
  • Enantioselective catalysis
  • Machine learning

ASJC Scopus subject areas

  • Chemistry(all)


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