We utilised pre-existing datasets for three chemical reactions: Nitro-Michael addition (ds1), Diels-Alder (ds2), and [3+2] cycloadditions (ds3) . Ds3 geometries were available at the DFT level of theory therefore we performed SQM calculations on the reported structures (see ESI, section 1.3 for full information). The newly created dataset is a collection of dimethyl malonate Michael addition reactions (ds4) which are a class of reactions of particular importance in polyketide biosynthesis. Reactant and TS geometries were generated for 1000 dimethyl malonate Michael addition reactions (Fig. ) using Schrödinger's R-Group enumeration tool. In this, we varied four positions on the α,βunsaturated carbonyl Michael acceptor (MA) using synthetically relevant and accessible functional groups. Once generated, Schrödinger's MacroModel 47 was used to conformationally search reactant and TSs using the OPLS3e forcefield. The lowest energy conformation for every structure was then optimised with AM1 and ωB97X-D/def2-TZVP. Single point energy (SPE) calculations at the same levels of theory were performed on these structures to account for solvent effects using the integral equation formalism of the polarisable continuum model (IEFPCM) with water. All calculations were performed with Gaussian16 (Revision A.03 and Revision C.01 ). Energies and quasi-harmonic free-energies (298.15 K, 1 mol l -1 concentration) were calculated using GoodVibes. Further details on this dataset are provided in the ESI, section 1.4. For the distortion/interaction analysis, the distorted reactants at the TS must be separated to perform the necessary calculations. Whilst there is python code available for this function, it requires a detailed input file about the given chemical system and does not provide a mapping of atom numbers from a TS to its respective reactant and distorted structures. We developed an approach that utilises the frequency vibrations of the TS as detailed in Fig. (the code is available on GitHub (group/distortion-interaction_ML)). The main benefit of our code is that it can provide us with a mapping of atom numbers between the reactant, distorted, and TS structures regardless of the initial atom numbering. This is crucial for ML approaches that use knowledge of specific atoms. Dataset ds3 contained arbitrary atom numbering and our approach for determining the common atoms of this dataset is summarised in the ESI, section 1.3. After extracting the distorted structures from each TS, SPE calculations were performed at the same level of theory that the TS and reactants were calculated at and in solvent with energies then extracted with GoodVibes. The distortion and interaction energies were calculated as outlined in ESI section 2.