  Machine learning techniques have recently been adopted in various
applications in medicine, biology, chemistry, and material engineering. An
important task is to predict the properties of molecules, which serves as the
main subroutine in many downstream applications such as virtual screening and
drug design. Despite the increasing interest, the key challenge is to construct
proper representations of molecules for learning algorithms. This paper
introduces the N-gram graph, a simple unsupervised representation for
molecules. The method first embeds the vertices in the molecule graph. It then
constructs a compact representation for the graph by assembling the vertex
embeddings in short walks in the graph, which we show is equivalent to a simple
graph neural network that needs no training. The representations can thus be
efficiently computed and then used with supervised learning methods for
prediction. Experiments on 60 tasks from 10 benchmark datasets demonstrate its
advantages over both popular graph neural networks and traditional
representation methods. This is complemented by theoretical analysis showing
its strong representation and prediction power.
