Both structure information and DDIs are important for GNN.

(A) The sight of GNNs in the second layer is shown in blue as we take the carbon with orange as the center. In this example, a GNN with two layers fails to identify the ring structure of zearalenone. (B) The GNN should preserve local structure information (orange ellipse) (C) The interaction type of ‘blood calcium increased’ between drug pair ‘Carnitine’ and ‘Budesonide’ is caused by their partial significant substructures (elliptical parts).</p

Atom and bond features.

Atom and bond features.</p

The significant difference between DGNN-DDI and other models in terms of predicted scores.

The significant difference between DGNN-DDI and other models in terms of predicted scores.</p

The key substructures contributing to the SARS-CoV-2 drug combinations.

The center of the most important substructure and its receptive field are shown as red circle and green colors respectively.</p

Performance for each DDI type.

Performance for each DDI type.</p

Investigating the contributions of substructure-attention mechanism and co-attention layer.

Investigating the contributions of substructure-attention mechanism and co-attention layer.</p

Analysis of the substructure attention mechanism (SA) and co-attention layer (CA).

(A)-(C) The metric scores of DGNN-DDI and without SA and/or CA. (D)-(F) The training and testing losses for DGNN-DDI and without SA and/or CA.</p

Violin plots displaying metric scores of all models.

Violin plots displaying metric scores of all models.</p

Drug combination to treat SARS-CoV-2.

Drug combination to treat SARS-CoV-2.</p

The overall computational steps for graph-level representation of <i>d</i>_<i>x</i> and <i>d</i>_<i>y</i>.

The overall computational steps for graph-level representation of dx and dy.</p