Molecular Structure-Driven Multi-Relation DGI Prediction With High-Low-Order Attention Denoise

Drug-Gene Interaction (DGI) is crucial for drug discovery and personalized medicine. The continuous development of genomics and drug repositioning has brought increasing attention to the complex relations between drugs and genes. However, traditional biological experiments are time-consuming and costly, which makes it challenging to efficiently explore the multi-relational interactions between drugs and genes. Therefore, computational approaches aim to develop efficient schemes for predicting drug-gene relations to reduce the search space and experimental costs. Existing computational methods often suffer from data scarcity and poor generalization, which pose significant challenges for practical applications. To address these issues, we propose a novel multi-relation DGI prediction method based on molecular structure-driving and high-low-order attention denoising framework. Our approach captures molecular structural information through both atom and bond channels with a drug feature encoder. For network structure, we enhance both high- and low-order channels: the low-order channel leverages graph convolutional networks, while the high-order channel employs hypergraph-based message propagation. Additionally, we adopt consistency information loss and inter-channel attention mechanism to refine high- and low-order features. Experimental results on three drug-gene datasets demonstrate the superior performance of our model, particularly on sparse datasets DrugBank and DGIdb, with F1 improvements of 4.06% and 5.67%, respectively.