Multi-Class Metabolic Pathway Prediction by Graph Attention-Based Deep Learning Method

Exploring the relationship between molecular structure and metabolic pathways plays a significant role in researching the metabolism and pharmacokinetic effects of drugs. Therefore, one usually want to know what kinds of metabolic pathways that a new synthetic drug compound may involve in. In this paper, we propose a novel deep learning framework based on GAT (Graph Attention neTwork) for such purpose. The framework mainly consists of a two-branch feature extractor and a FC (Fully Connected) layer. In the two-branch feature extractor, one is used to generate three kinds global features of the compounds, and the other one is used to learn the local structural features via two GAT layers. All features are embedded into the FC layer to output multiple probabilities, each of which corresponds to the probability of one kind of pathway that the compound belongs to. The comparing results to other five state-of-the-art representative methods on the KEGG (Kyoto Encyclopedia of Genes and Genomes) data set have shown that our method can achieve the highest prediction accuracy, illustrating that it is a promising tool that can be helpful to analyze the metabolic pathways of the drug compounds.