Molecular field analysis for data-driven molecular design in asymmetric catalysis
Shigeru Yamaguchi
Abstract
This review highlights the recent advances (2019-present) in the use of MFA (molecular field analysis) for data-driven catalyst design, enabling to improve selectivities/reaction outcomes in asymmetric catalysis. Successful examples of MFA-based molecular design and how to design molecules by MFA are described, including how to generate and evaluate MFA-based regression models, and future challenges in MFA-based molecular design in molecular catalysis.
Topics & Concepts
ChemistryCatalysisMolecular modelEnantioselective synthesisNanotechnologyField (mathematics)Biochemical engineeringOrganic chemistryEngineeringMaterials sciencePure mathematicsMathematicsMachine Learning in Materials ScienceComputational Drug Discovery MethodsAsymmetric Hydrogenation and Catalysis