AWESOM comprises three core modules: convolutional, molecular context-pooling, and classification. The convolutional module includes three GINE layers of size 480. The first two layers are followed by batch normalization and LeakyReLU activation. The molecular context-pooling module computes graph-level representations using additive pooling. This pooled representation is added to all nodes in the graph, effectively doubling the dimensionality of the node features. The classification module consists of a fully-connected layer of size 268, followed by batch normalization, LeakyReLU activation, and a final fully connected layer. This last layer maps the latent representation to a single output, which is subsequently transformed into a probabilistic output via the sigmoid function. The architecture is depicted schematically in Figure .