Our GNN is similar to the one developed by Han et al. and is briefly described as follows. Let G be the graph corresponding to a given molecule. To vertex v i ∈ V , assign an initial feature vector v i , and to the edge connecting v i and v j assign a feature vector e i j . A GNN uses a sequence of parameterised functions to estimate a vector of atom properties y = (y 1 , y 2 , . . . , y n ), where n is the number of atoms in the molecule. The parameters in these functions are optimized to minimise prediction errors compared to a set of training data. In our case, y i is the ATC for atom i. In a GNN, the prediction of y is achieved in several steps. In the first step, the initial feature vector v i is transformed into M so-called embedding vectors h 0,1 i , h 0,2 i , . . . , h 0,M i , where