  Effective molecular representation learning is of great importance to
facilitate molecular property prediction, which is a fundamental task for the
drug and material industry. Recent advances in graph neural networks (GNNs)
have shown great promise in applying GNNs for molecular representation
learning. Moreover, a few recent studies have also demonstrated successful
applications of self-supervised learning methods to pre-train the GNNs to
overcome the problem of insufficient labeled molecules. However, existing GNNs
and pre-training strategies usually treat molecules as topological graph data
without fully utilizing the molecular geometry information. Whereas, the
three-dimensional (3D) spatial structure of a molecule, a.k.a molecular
geometry, is one of the most critical factors for determining molecular
physical, chemical, and biological properties. To this end, we propose a novel
Geometry Enhanced Molecular representation learning method (GEM) for Chemical
Representation Learning (ChemRL). At first, we design a geometry-based GNN
architecture that simultaneously models atoms, bonds, and bond angles in a
molecule. To be specific, we devised double graphs for a molecule: The first
one encodes the atom-bond relations; The second one encodes bond-angle
relations. Moreover, on top of the devised GNN architecture, we propose several
novel geometry-level self-supervised learning strategies to learn spatial
knowledge by utilizing the local and global molecular 3D structures. We compare
ChemRL-GEM with various state-of-the-art (SOTA) baselines on different
molecular benchmarks and exhibit that ChemRL-GEM can significantly outperform
all baselines in both regression and classification tasks. For example, the
experimental results show an overall improvement of 8.8% on average compared to
SOTA baselines on the regression tasks, demonstrating the superiority of the
proposed method.
