multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction.

Motivation: Accurate identification of proteins interacted with drugs helps reduce the time and cost of drug development. Most of previous methods focused on integrating multisource data about drugs and proteins for predicting drug-target interactions (DTIs). There are both similarity connection and interaction connection between two drugs, and these connections reflect their relationships from different perspectives.

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

Motivation: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors.

Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.

Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data about drugs and targets, however, is a challenging issue. We propose a drug-target interaction (DTI) prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections.