  A graph neural network (GNN) is a good choice for predicting the chemical
properties of molecules. Compared with other deep networks, however, the
current performance of a GNN is limited owing to the "curse of depth." Inspired
by long-established feature engineering in the field of chemistry, we expanded
an atom representation using Weisfeiler-Lehman (WL) embedding, which is
designed to capture local atomic patterns dominating the chemical properties of
a molecule. In terms of representability, we show WL embedding can replace the
first two layers of ReLU GNN -- a normal embedding and a hidden GNN layer --
with a smaller weight norm. We then demonstrate that WL embedding consistently
improves the empirical performance over multiple GNN architectures and several
molecular graph datasets.
