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

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.

9 pages

References in corpus (3)

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
• Message-passing neural networks for high-throughput polymer screening

Cited by in corpus (7)

• A Comprehensive Survey on Deep Graph Representation Learning
• Towards Semi-supervised Universal Graph Classification
• KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
• Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
• A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction
• A Simple Spectral Failure Mode for Graph Convolutional Networks
• A Structural Feature-Based Approach for Comprehensive Graph Classification