Triangular Contrastive Learning on Molecular Graphs

TL;DR

This paper introduces TriCL, a novel multimodal contrastive learning framework utilizing a Triangular Area Loss to improve the quality of molecular graph representations, leading to better downstream property prediction.

Contribution

The paper proposes TriCL, a universal trimodal contrastive learning framework with a novel Triangular Area Loss that enhances embedding space geometry for molecular data.

Findings

TriCL outperforms existing methods on molecular property prediction tasks.

Triangular Area Loss effectively addresses line-collapsing in embedding space.

Embedding space improvements lead to better downstream task performance.

Abstract

Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances. Regarding the multifaceted nature of large unlabeled data used in self-supervised learning while majority of real-word downstream tasks use single format of data, a multimodal framework that can train single modality to learn diverse perspectives from other modalities is an important challenge. In this paper, we propose TriCL (Triangular Contrastive Learning), a universal framework for trimodal contrastive learning. TriCL takes advantage of Triangular Area Loss, a novel intermodal contrastive loss that learns the angular geometry of the embedding space through simultaneously contrasting the area of positive and negative triplets. Systematic observation on embedding space in terms of alignment and uniformity showed that Triangular Area Loss can address the line-collapsing problem by discriminating modalities by angle. Our experimental results also demonstrate the outperformance of TriCL on downstream task of molecular property prediction which implies that the advantages of the embedding space indeed benefits the performance on downstream tasks.