Before revision:  One promising way of doing this is exploiting the molecular graph structure , which leads to several graph-based deep learning models, such as Graph Neural Networks ( GNN), Message Passing Networks (MPN), etc. However, current deep graph learning models generally focus on either node embedding or edge embedding. Since both atoms and chemical bonds play important roles in chemical reactions and reflect the property of molecules, existing methods fail to exploit both node (atom) and edge (bond) features simultaneouslyto make property prediction.
After revision:  One promising way of doing this is exploiting the molecular graph structure , which leads to several graph-based deep learning models, such as Graph Neural Networks ( GNNs). It is well known that both atoms and chemical bonds play important roles in chemical reactions and reflect the property of molecules, existing methods fail to exploit both node (atom) and edge (bond) features simultaneouslyto make property prediction.