  Molecular property prediction (e.g., energy) is an essential problem in
chemistry and biology. Unfortunately, many supervised learning methods usually
suffer from the problem of scarce labeled molecules in the chemical space,
where such property labels are generally obtained by Density Functional Theory
(DFT) calculation which is extremely computational costly. An effective
solution is to incorporate the unlabeled molecules in a semi-supervised
fashion. However, learning semi-supervised representation for large amounts of
molecules is challenging, including the joint representation issue of both
molecular essence and structure, the conflict between representation and
property leaning. Here we propose a novel framework called Active
Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and
unlabeled molecules. Specifically, ASGN adopts a teacher-student framework. In
the teacher model, we propose a novel semi-supervised learning method to learn
general representation that jointly exploits information from molecular
structure and molecular distribution. Then in the student model, we target at
property prediction task to deal with the learning loss conflict. At last, we
proposed a novel active learning strategy in terms of molecular diversities to
select informative data during the whole framework learning. We conduct
extensive experiments on several public datasets. Experimental results show the
remarkable performance of our ASGN framework.
