Hierarchical Molecular Graph Self-Supervised Learning for property prediction

Abstract

Graph Neural Networks are employed to encode molecular graph representations, but structural information and chemical functions are largely missing. Here, the authors develop hierarchical molecular graph self-supervised learning as a framework to learn molecule representation for property prediction.