  The recent success of graph neural networks has significantly boosted
molecular property prediction, advancing activities such as drug discovery. The
existing deep neural network methods usually require large training dataset for
each property, impairing their performances in cases (especially for new
molecular properties) with a limited amount of experimental data, which are
common in real situations. To this end, we propose Meta-MGNN, a novel model for
few-shot molecular property prediction. Meta-MGNN applies molecular graph
neural network to learn molecular representation and builds a meta-learning
framework for model optimization. To exploit unlabeled molecular information
and address task heterogeneity of different molecular properties, Meta-MGNN
further incorporates molecular structure, attribute based self-supervised
modules and self-attentive task weights into the former framework,
strengthening the whole learning model. Extensive experiments on two public
multi-property datasets demonstrate that Meta-MGNN outperforms a variety of
state-of-the-art methods.
