AWESOM comprises three core modules: convolutions, molecular context-pooling, and classification. The convolutional module includes three graph convolutional layers of size 480. The first two layers are followed by batch normalization and LeakyReLU activation . The chosen graph convolutional operator is the modified graph isomorphism network (GIN) operator proposed by Hu et al. , referred to as the GINE operator. The molecular context-pooling module computes graph-level representations using additive pooling. This pooled representation is concatenated 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 complete list of employed node and edge features as well as a schematic depiction of AWESOM's architecture can be found in the Supporting Information, sections S.3 and S.5.