Exploring Graph Neural Networks for Molecular Activity Prediction

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful class of machine learning techniques capable of processing graph-structured data, showing immense promise in molecular property prediction. This thesis compares the performance of two specific GNNs—Graph Attention Networks (GATs) and Attentive FP models with Graph Convolutional Networks (GCNs) in predicting the biological activities of protein targets across several protein families. Our findings indicate that while GCNs are highly effective for molecular property prediction, GATs and Attentive FP models also offer competitive performance, with GATs showing particular promise for enzymes and transporters. The experiments suggest that the choice of model should be tailored to specific families of target proteins, highlighting the need to consider the particular protein family when selecting GNNs for predictive modeling in drug discovery.