Additionally, a detailed analysis of conformational space may be necessary to identify the most important stationary points on the potential energy surface of both the parent molecule and the two radicals formed upon homolysis. These practical limitations have led to the development of alternative approaches for BDE estimation, such as Quantitative Structure-Property Relationship (QSPR) and Machine Learning (ML) models. A machine Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET) achieved an MAE of 0.58 kcal mol -l (vs. an M06-2X/def2-TZVPP oracle) for BDEs of unseen molecules containing C, H, N, O atoms using a message-passing Graph-convolutional Neural Network (GNN), and has found utility in multiple applications. Rapid and accurate predictions of BDEs have enabled the application of ML to various domains of chemistry including biological metabolism and combustion chemistry.