Molecular Contrastive Learning of Representations via Graph Neural Networks

Understand

Molecular Machine Learning (ML) bears promise for efficient molecule property prediction and drug discovery.

• However, labeled molecule data can be expensive and time-consuming to acquire.
• Due to the limited labeled data, it is a great challenge for supervised-learning ML models to generalize to the giant chemical space.
• In this work, we present MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework that leverages large unlabeled data (~10M unique molecules).