As demonstrated, our approach involves incorporating distance features into multigraph reasoning models, which are inherently non-Euclidean . These models primarily establish relations among graph elements and often overlook the significance of distance features, which, in contrast, play a crucial role in protein-ligand binding interactions. Nevertheless, graph convolutional neural networks (GCNs) are highly effective at capturing intricate patterns in the graph's structured data, and they showed remarkable results in predicting binding affinity . Therefore, we exploit the strengths of GCNs and incorporate the distance features to enhance their capabilities further.