Dataset for "Machine learning and semi-empirical calculations: A synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction"

Modern quantum mechanical modelling methods, such as Density Functional Theory (DFT), have provided detailed mechanistic insights into countless reactions and have been used in the design of a handful of chemical transformations. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming Nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that achieves the fast and accurate prediction of DFT-quality free energy activation barriers using purely SQM-derived data. This dataset includes all the structural data, in the form of Gaussian16 (Revision A.03) output files, for the Nitro-Michael reaction used for this machine learning analysis.Uncatalysed reactant and transition state geometries for 1000 Nitro-Michael addition reactions were built using Schrödinger’s R-Group Enumeration by varying at four positions of a generic Michael acceptor core with common organic fragments. In addition to the nucleophile, uncatalysed reactant and transition state geometries for a further 37 biologically important Nitro-Michael addition reactions from literature were built in Gaussian16 (Revision A.03). All reactant and transition state structures were conformationally searched using Schrödinger’s MacroModel (version 12.7). All structures were subsequently optimised using Gaussian16 (Revision A.03) using several different molecular modelling methods