Chemistry

Hierarchical Molecular Graph Self-Supervised Learning for property prediction

X. Zang, X. Zhao, et al.

This exciting research introduces HiMol, a pre-training framework that leverages Hierarchical Molecular Graph Neural Networks to decode intricate molecular structures and predict their properties. Conducted by Xuan Zang, Xianbing Zhao, and Buzhou Tang, this work demonstrates remarkable effectiveness in understanding chemical semantics through innovative self-supervised learning techniques.