ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

TL;DR

This paper introduces ASGN, a semi-supervised graph neural network framework for molecular property prediction that effectively leverages unlabeled data and active learning to improve prediction accuracy in the face of scarce labeled molecules.

Contribution

The paper proposes a novel teacher-student semi-supervised framework with an active learning strategy based on molecular diversity for improved molecular property prediction.

Findings

ASGN outperforms existing methods on public datasets.

The active learning strategy enhances data efficiency.

Semi-supervised learning improves representation quality.

Abstract

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.