Prediction of Bond Dissociation Energy for Organic Molecules Based on a Machine-Learning Approach

Bond dissociation energy (BDE), which refers to the enthalpy change for the homolysis of a specific covalent bond, is one of the basic thermodynamic properties of molecules. It is very important for understanding chemical reactivities, chemical properties and chemical transformations. Here, a machine learning-based comprehensive BDE prediction model was established based on the iBonD experimental BDE dataset and the calculated BDE dataset by St. John et al. Differential Structural and PhysicOChemical (D-SPOC) descriptors that reflected changes in molecules' structural and physicochemical features in the process of bond homolysis were designed as input features. The model trained with LightGBM algorithm gave a low mean absolute error (MAE) of 1.03 kcal/mol on the test set. The D-SPOC model could apply to accurate BDE prediction of phenol O-H bonds, uncommon N-SCF3 and O-SCF3 reagents, and beta-C-H bonds in enamine intermediates. A fast online prediction platform was constructed based on the D-SPOC model, which could be found at .