3D graph contrastive learning for molecular property prediction

Abstract

Motivation 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. Results 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 0.5 million model parameters, we achieved state-of-the-art or comparable performance in six benchmark datasets. Extensive experiments demonstrate that 3D structural information based on chemical knowledge is essential to molecular representation learning for property prediction. Availability Data and codes are available in https://github.com/moonkisung/3DGCL.

LGGA-MPP: Local Geometry-Guided Graph Attention for Molecular Property Prediction

“…Many existing self-supervised learning methods focus solely on the 2D structure of molecular graphs; the influence of geometric structures on molecular properties is often disregarded. Recent research studies have proposed pretraining methods that incorporate molecular geometry, intending to infuse geometric knowledge into 2D molecules. − Nevertheless, effectively utilizing geometric information to improve model performance is still a challenge.…”