About this scholarly article

Coordinating Cross-modal Distillation for Molecular Property Prediction by Zhang, Hao; Zhang, Nan; Zhang, Ruixin; Shen, Lei; Zhang, Yingyi; Liu, Meng is a scholarly article available to read on EtoBox.

In recent years, molecular graph representation learning (GRL) has drawn much more attention in molecular property prediction (MPP) problems. The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP. However, accurate 3D structures are often costly and time-consuming to obtain, limiting the large-scale application of GRL. It is an intuitive solution to train with 3D to 2D knowledge distillation and predict with only 2D inputs. But some challenging problems remain open for 3D to 2D distillation. One is that the 3D view is quite distinct from the 2D view, and the other is that the gradient magnitudes of atoms in distillation are discrepant and unstable due to the variable molecular size. To address these challenging problems, we exclusively propose a distillation framework that contains global molecular distillation and local atom distillation. We also provide a theoretical insight to justify how to coordinate atom and molecular information, which tackles the drawback of variable molecular size for atom information distillation. Experimental results on two popular molecular datasets demonstrate that our proposed model ach

Author

Zhang, Hao; Zhang, Nan; Zhang, Ruixin; Shen, Lei; Zhang, Yingyi; Liu, Meng

Published

2022

Language

EN