  The generation of graph-structured data is an emerging problem in the field
of deep learning. Various solutions have been proposed in the last few years,
yet the exploration of this branch is still in an early phase. In sequential
approaches, the construction of a graph is the result of a sequence of
decisions, in which, at each step, a node or a group of nodes is added to the
graph, along with its connections. A very relevant application of graph
generation methods is the discovery of new drug molecules, which are naturally
represented as graphs. In this paper, we introduce a sequential molecular graph
generator based on a set of graph neural network modules, which we call
MG^2N^2. Its modular architecture simplifies the training procedure, also
allowing an independent retraining of a single module. The use of graph neural
networks maximizes the information in input at each generative step, which
consists of the subgraph produced during the previous steps. Experiments of
unconditional generation on the QM9 dataset show that our model is capable of
generalizing molecular patterns seen during the training phase, without
overfitting. The results indicate that our method outperforms very competitive
baselines, and can be placed among the state of the art approaches for
unconditional generation on QM9.
