Computational Study on Data Integration in Chiral Catalyst De-sign: 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 datasets in which substituents are varied on a single catalyst or substrate scaffold. Here, we present data integration for chiral catalyst design using MFA (molecular field analysis), with voxel descriptors derived from 718 TS (transition-state) struc-tures calculated through DFT methods, along with their corresponding computed ΔΔG‡ values (energy differences between TSs leading to each enantiomer). These datasets covered seven reaction types, including organocatalysis, transition-metal catalysis, and both Michael addition and Diels–Alder reactions. MFA using integrated datasets from seven pairs of distinct reaction systems enabled the design of chiral catalysts with improved computed enantioselectivity in all the seven catalytic systems. To facilitate applications, we have released a web platform (https://mcds.riken.jp) that offers an MFA-based design tool and a database of DFT-computed TS data.