OBJECTIVES: To develop a heterogeneous graph prediction method based on the fusion of multi-layer semantics and topological information for addressing the challenges in drug-target interaction prediction, including insufficient modeling of high-order semantic dependencies, lack of adaptive fusion of semantic paths, and over-smoothing of node features. METHODS: A heterogeneous graph network with multiple types of entities such as drugs, proteins, side effects, and diseases was constructed, and graph embedding techniques were used to obtain low-dimensional feature representations. An adaptive metapath search module was introduced to automatically discover semantic path combinations for guiding the propagation of high-order semantic information. A semantic aggregation mechanism integrating multi-head attention was designed to automatically learn the importance of each semantic path based on contextual information and achieve differentiated aggregation and dynamic fusion among paths. A structure-aware gated graph convolutional module was then incorporated to regulate the feature propagation intensity for suppressing redundant information and redcuing over-smoothing. Finally, the potential interactions between drugs and targets were predicted through an inner product operation. RESULTS: Compared with existing drug-target interaction prediction methods, the proposed method achieved an average improvement of 3.4% and 2.4%, 3.0% and 3.8% in terms of the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) on public datasets, respectively. CONCLUSIONS The drug-target interaction prediction method developed in this study can effectively extract complex high-order semantic and topological information from heterogeneous biological networks, thereby improving the accuracy and stability of drug-target interaction prediction. This method provides technical support and theoretical foundation for precise drug target discovery and targeted treatment of complex diseases.
Semantics ; Humans ; Drug Interactions ; Neural Networks, Computer ; Algorithms

In the microscale, bacteria with helical body shapes have been reported to yield advantages in many bio-processes. In the human society, there are also wisdoms in knowing how to recognize and make use of helical shapes with multi-functionality. Herein, we designed atypical chiral mesoporous silica nano-screws (CMSWs) with ideal topological structures (e.g., small section area, relative rough surface, screw-like body with three-dimension chirality) and demonstrated that CMSWs displayed enhanced bio-adhesion, mucus-penetration and cellular uptake (contributed by the macropinocytosis and caveolae-mediated endocytosis pathways) abilities compared to the chiral mesoporous silica nanospheres (CMSSs) and chiral mesoporous silica nanorods (CMSRs), achieving extended retention duration in the gastrointestinal (GI) tract and superior adsorption in the blood circulation (up to 2.61- and 5.65-times in AUC). After doxorubicin (DOX) loading into CMSs, DOX@CMSWs exhibited controlled drug release manners with pH responsiveness in vitro. Orally administered DOX@CMSWs could efficiently overcome the intestinal epithelium barrier (IEB), and resulted in satisfactory oral bioavailability of DOX (up to 348%). CMSWs were also proved to exhibit good biocompatibility and unique biodegradability. These findings displayed superior ability of CMSWs in crossing IEB through multiple topological mechanisms and would provide useful information on the rational design of nano-drug delivery systems.