Molecular contrastive learning of representations via graph neural networks

Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani

Abstract: Molecular representations are hard to design due to the large size of the chemical space, the amount of potentially important information in a molecular structure and the relatively low number of annotated molecules. Still, the quality of these representations is vital for computational models trying to predict molecular properties. Wang et al. present a contrastive learning approach to provide differentiable representations from unlabelled data.