  Molecule property prediction is a fundamental problem for computer-aided drug
discovery and materials science. Quantum-chemical simulations such as density
functional theory (DFT) have been widely used for calculating the molecule
properties, however, because of the heavy computational cost, it is difficult
to search a huge number of potential chemical compounds. Machine learning
methods for molecular modeling are attractive alternatives, however, the
development of expressive, accurate, and scalable graph neural networks for
learning molecular representations is still challenging. In this work, we
propose a simple and powerful graph neural networks for molecular property
prediction. We model a molecular as a directed complete graph in which each
atom has a spatial position, and introduce a recursive neural network with
simple gating function. We also feed input embeddings for every layers as skip
connections to accelerate the training. Experimental results show that our
model achieves the state-of-the-art performance on the standard benchmark
dataset for molecular property prediction.
