Three undirected graph convolutional networks (GCNs) are trained to reproduce the energy storage, absorption, and TBR barrier. The GCN model is essentially that implemented in DeepChem and written in Python 3.6.8 using PyTorch version 1.2.0 and PyTorch Geometric version 1.3.2. The feature vectors of the nodes describe the atom type, number of directly bonded neighbors, number of hydrogen atoms attached to the atom, formal charge, hybridization, and aromaticity, while the feature vectors of the edges include bond orders as well as information about conjugation and presence in a ring system. The GCN uses two graph convolutional layers, with 128 channels in the first layer and 64 in the second. After the final graph convolutional layer, a global max-pooling layer creates a representation with 64 values, which is fed to the feed-forward neural network. The feed-forward network has two hidden layers with 64 nodes in each layer. The final layer outputs a single value i.e. the target value predicted by the network. F F F N S Figure : An example of a NBD/QC derivative included in the chemical space defined in Fig. . The gene for this system is "[['C',17,0,0,0,0],['A',3,0,0,0,0],['A',0,0,0,0,0],['B',0,0,2,0,0]]", and the smile is