In this work, we utilize Chemprop, a state-of-the-art GNN model, for the prediction of activation energies. Chemprop processes molecular structures using SMILES strings to generate unique molecular descriptors. The model employs a D-MPNN, a type of GNN, to construct these descriptors. These descriptors, represented as vectors, are subsequently passed through a feed-forward neural network (FFNN) for target property prediction as shown in Figure . Chemprop begins by describing molecules based on their fundamental properties and atomic connections. The atomic features include onehot encodings of atomic number, number of bonds, formal charge, chirality, number of hydrogens, hybridization, aromaticity, and scaled atomic mass. Bond features, such as bond type, bond conjugation, ring structure, and stereochemical information (e.g., cis/trans configuration), are also incorporated. This information is stored within the atom and bond features, which serve as the vertices and edges of the graph neural network. These features are iteratively updated through message passing with neighboring atoms, creating an informative representation of each atom. After this exchange of information, the features are aggregated to form a comprehensive molecular representation.