  Self-supervised learning (SSL) is a method that learns the data
representation by utilizing supervision inherent in the data. This learning
method is in the spotlight in the drug field, lacking annotated data due to
time-consuming and expensive experiments. SSL using enormous unlabeled data has
shown excellent performance for molecular property prediction, but a few issues
exist. (1) Existing SSL models are large-scale; there is a limitation to
implementing SSL where the computing resource is insufficient. (2) In most
cases, they do not utilize 3D structural information for molecular
representation learning. The activity of a drug is closely related to the
structure of the drug molecule. Nevertheless, most current models do not use 3D
information or use it partially. (3) Previous models that apply contrastive
learning to molecules use the augmentation of permuting atoms and bonds.
Therefore, molecules having different characteristics can be in the same
positive samples. We propose a novel contrastive learning framework,
small-scale 3D Graph Contrastive Learning (3DGCL) for molecular property
prediction, to solve the above problems. 3DGCL learns the molecular
representation by reflecting the molecule's structure through the pre-training
process that does not change the semantics of the drug. Using only 1,128
samples for pre-train data and 1 million model parameters, we achieved the
state-of-the-art or comparable performance in four regression benchmark
datasets. Extensive experiments demonstrate that 3D structural information
based on chemical knowledge is essential to molecular representation learning
for property prediction.
