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

Comprehensive Summary 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 i BonD experimental BDE dataset and the calculated BDE dataset by St. John et al . D ifferential S tructural and P hysic OC hemical (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‐SCF 3 and O‐SCF 3 reagents, and β ‐C—H bonds in enamine intermediates. A fast online prediction platform was constructed based on the D‐SPOC model, which could be found at http://isyn.luoszgroup.com/bde_prediction .