Fig. 1: The architecture of GraphBAN.
From: GraphBAN: An inductive graph-based approach for enhanced prediction of compound-protein interactions
a The input compound molecules are encoded by graph convolutional network (GCN) layers and ChemBERTa separately, while protein sequences are encoded by 1D layer convolutional neural network (CNN) and evolutionary scale modeling (ESM). b Fusion of features with same architecture for both compounds and proteins. Fusion module shows how we bring the extracted features in the same dimensionality with the Linear Transform Layers. The feature fusion includes two “MatMul” layers that operate element-wise multiplication and one “Addition” layer that do the element-wise addition. c The CDAN module. It receives input from the bilinear attention network (BAN) layer and generates concatenation of the compound and protein features and SoftMax logits “g” for source and target domains into a joint conditional representation using the discriminator module. The discriminator has two fully connected layers with an adversarial loss to minimize the classification error between the source and target domains.
