Molecular Classification with Graph ConvolutionalNetworks: Exploring the MUTAG Dataset for Mutagenicity Prediction

Abstract

This paper presents the implementation of a Graph Convolutional Network (GCN) for the classification of chemical compounds using the MUTAG dataset, which consists of 188 ni- troaromatic compounds labeled according to their mutagenicity. The GCN model leverages the inherent graph structure of molec-ular data to capture and learn from the relationships between atoms and bonds, represented as nodes and edges, respectively. By utilizing three graph convolutional layers followed by a global mean pooling layer, the model effectively aggregates node features to generate meaningful graph-level representations. The model was trained using the Adam optimizer with a learning rate of 0.01, and cross-entropy loss was employed to supervise the classification task. The results demonstrate the efficacy of GCNs in graph classification tasks, with the model achieving a training accuracy of 79.33% and a test accuracy of 76.32%. This study highlights the potential of GCNs in cheminformatics and other domains where graph-structured data is prevalent, paving the way for further exploration and application of advanced graph neural networks in similar tasks.