  Neural methods of molecule property prediction require efficient encoding of
structure and property relationship to be accurate. Recent work using graph
algorithms shows limited generalization in the latent molecule encoding space.
We build a Transformer-based molecule encoder and property predictor network
with novel input featurization that performs significantly better than existing
methods. We adapt our model to semi-supervised learning to further perform well
on the limited experimental data usually available in practice.
