Application of an Improved Graph Neural Network for Drug Property Prediction

The prediction of drug properties plays a vital role in drug research. However, the drug property prediction accuracy of traditional methods is limited due to their inability to fully capture molecular structure and function information. As a result, the use of graph neural networks has attracted significant attention as an effective drug property prediction approach. Nevertheless, traditional graph neural networks still exhibit certain drawbacks in this regard, including their disregard of the interaction information between nodes and edges, the loss of local information during global pooling operations, and the absence of feature fusion mechanisms. This study proposes an enhanced graph neural network (GNN) model that incorporates an attention mechanism, multiscale pooling, an adaptive weight generator, and an activation function to predict drug properties. A comparative analysis with the conventional graph neural network model reveals significant improvements in terms of predicting the side effects of drugs on the heart and liver, with increases of 1%, 7%, and 13%. Furthermore, the enhanced graph neural network model exhibits good performance across the remaining two datasets. Empirical findings underscore the efficacy of the model in drug property prediction tasks, and it is characterized by enhanced predictive precision and robust performance outcomes.