Computational Study on Data Integration in Chiral Catalyst Design: A Case Study Using Michael Addition Reactions

Data science in molecular catalysis often relies on the idea that molecules with similar structures show similar properties. This concept underlies many successful examples of regression analysis based on free energy relationships, typically using data sets in which substituents are varied on a single catalyst or substrate scaffold. Here, we present data integration for chiral catalyst design via regression using three-dimensional image-like descriptors (voxel data) derived from 718 TS (transition-state) structures calculated through DFT methods, along with their corresponding computed ΔΔ<i>G</i>^‡ values (energy differences between TSs leading to each enantiomer). These data sets covered seven reaction types, including organocatalysis, transition-metal catalysis, and both Michael addition and Diels-Alder reactions. Regression using integrated data sets from seven pairs of distinct reaction systems enabled the design of chiral catalysts with improved computed enantioselectivity in all seven catalytic systems. To facilitate applications, we have released a web platform (https://mcds.riken.jp) that offers a data-driven design tool and a database of DFT-computed TS data.