Molecular Property Prediction Based on Graph Structure Learning
Bangyi Zhao Weixia Xu Jihong Guan Shuigeng Zhou
Molecular property prediction (MPP) is a fundamental but challenging task in
the computer-aided drug discovery process. More and more recent works employ
different graph-based models for MPP, which have made considerable progress in
improving prediction performance. However, current models often ignore
relationships between molecules, which could be also helpful for MPP. For this
sake, in this paper we propose a graph structure learning (GSL) based MPP
approach, called GSL-MPP. Specifically, we first apply graph neural network
(GNN) over molecular graphs to extract molecular representations. Then, with
molecular fingerprints, we construct a molecular similarity graph (MSG).
Following that, we conduct graph structure learning on the MSG (i.e.,
molecule-level graph structure learning) to get the final molecular embeddings,
which are the results of fusing both GNN encoded molecular representations and
the relationships among molecules, i.e., combining both intra-molecule and
inter-molecule information. Finally, we use these molecular embeddings to
perform MPP. Extensive experiments on seven various benchmark datasets show
that our method could achieve state-of-the-art performance in most cases,
especially on classification tasks. Further visualization studies also
demonstrate the good molecular representations of our method.