AWESOM's architecture consists of three GINE graph convolutional layers, each of size 480, with batch normalization and LeakyReLU activation applied between layers. Molecular context is incorporated by computing a graph-level representation using additive pooling, which is then concatenated with the node representations. Classification is performed by a multi-layer perceptron module that transforms the obtained latent representation into a probabilistic output with the sigmoid function. After thorough hyperparameter tuning, the model was trained 50 times with different random seeds to generate an ensemble. The specifics are provided in the Methods, sections 2.3 and 2.4.