In recent years, the "drug-target-disease" association has become increasingly complex and data-scarce. Existing methods are limited by information loss caused by explicit graph construction when modeling triplet relationships, making it difficult to effectively capture geometric structures and long-range dependencies, thereby affecting prediction performance. To address this, this paper proposes a new method based on point cloud modeling-TriCloud. This method represents each triad as a spatial point cloud, encoding semantic and topological relationships through geometric coordinates, and performs feature learning directly on an unordered point set, avoiding reliance on predefined graph structures. Based on the PointNet architecture, it introduces a multi-view feature extraction and fusion mechanism to enhance the modeling capability of global structures and complex spatial patterns. Experimental results show that TriCloud significantly outperforms existing methods on multiple benchmark datasets, achieving an AUC of 0.9995 and an AUPR of 0.9996, with all metrics ranking first. External validation demonstrates its excellent generalization ability. Feature analysis reveals that the geometric-semantic joint features of positive samples play a dominant role in classification. This study provides an efficient and reliable computational framework for drug repurposing, contributing to the development of precision medicine.

Drug repurposing relies critically on the accurate prediction of drug-target interactions (DTIs). Conventional graph neural network approaches typically model drugs and proteins as isolated nodes, focusing solely on intrinsic attributes such as molecular structure or sequence information. As a result, they often fail to capture complex synergistic effects-such as multi-target regulation. To overcome this limitation, this paper proposes the Topology-Aggregation Enhanced Graph Interaction Network (TAGIN-DTI), a novel framework that shifts the prediction paradigm from a conventional node-level view to a subnetwork-level perspective. The model aggregates topological association features from drug-drug and protein-protein interaction networks via a dual-path Transformer encoder, integrates multi-scale global and local information through a gated fusion mechanism, and incorporates MinHash-based subgraph structure encoding to enhance neighborhood topological representation. Experimental results demonstrate that TAGIN-DTI outperforms existing methods in both prediction accuracy and generalization capability, offering valuable insights for drug repurposing and target discovery.

Drug repositioning is a strategy to identify new therapeutic uses for existing drugs, significantly reducing development costs and time. Although deep learning methods for drug-target interaction prediction have advanced, most models are limited to binary relationships and struggle to capture complex ternary associations among drugs, targets, and diseases. Additionally, limitations in graph structure modeling and node feature representation often constrain their generalization capability. To address these challenges, this paper proposes GraphTransHGN, a framework integrating graph embedding, Transformer feature extraction, and heterogeneous graph neural networks. First, Node2Vec constructs graph representations of drugs and targets, with the Transformer extracting high-level semantic features. Drug-target pairs are then combined into composite nodes (DT_node), and target-disease data are incorporated to form a heterogeneous graph. Finally, the HGTConv models multi-type relationships between DT_node and diseases, enabling end-to-end prediction of potential therapeutic associations. Experimental results demonstrate that GraphTransHGN outperforms mainstream methods across key metrics, achieving an AUC of 0.9916, Recall of 0.9959, and AUPR of 0.9849, which confirms its discriminative power and robustness. The model not only improves prediction accuracy for drug repositioning but also offers a novel technical and theoretical foundation for mechanism-based drug discovery.

BACKGROUND: Drug-drug interactions (DDIs) frequently occur in combination therapy and may cause adverse effects or reduced efficacy. Existing computational approaches often fail to capture both the semantic information in drug sequences and the structural properties of drug molecules, limiting predictive power. RESULTS: We propose MDG-DDI, a deep learning framework that integrates a Frequent Consecutive Subsequence (FCS)-based Transformer encoder with a Deep Graph Network (DGN) to extract complementary semantic and structural features. These representations are fused and fed into a Graph Convolutional Network (GCN) for DDI prediction. Experiments on three benchmark datasets under transductive and inductive settings show that MDG-DDI consistently outperforms state-of-the-art methods, with particularly strong gains when predicting interactions involving unseen drugs. CONCLUSION: By jointly modeling substructure-level semantics and molecular graph structure, MDG-DDI achieves robust and accurate DDI prediction. The framework demonstrates improved generalization and offers potential for enhancing drug safety assessment and discovery.

Drug repositioning is a vital area of biomedicine, where confirming interactions between drugs and specific targets is essential for establishing the efficacy of pharmaceutical agents. Traditional in vitro screening methods have limitations, prompting the use of computer simulations as an effective alternative for predicting drug-target interactions (DTI). This approach has gained significant attention in the scientific community. In this study, we introduce MFCM-DTI, a DTI prediction model that employs multimodal features to accurately capture the intricate interactions between drug molecular structures and key amino acids of target proteins. Our results demonstrate that MFCM-DTI outperforms existing models in prediction accuracy and robustness. Furthermore, MFCM-DTI has been successfully used to predict interactions between key SARS-CoV-2 proteins and existing drugs, providing a solid foundation for developing therapeutic agents against SARS-CoV-2 infection. This study underscores the broad applicability and strong predictive capabilities of MFCM-DTI in drug-protein interaction prediction, opening new avenues for research. The predicted drug targets and interaction data offer valuable insights for future experimental validation and clinical trials, potentially driving innovation in the biomedical field.