Drug repositioning can effectively reduce research and development costs and accelerate time to market by identifying new indications for existing drugs. In recent years, deep learning-based methods have achieved remarkable progress in the field of drug repositioning. However, current approaches still suffer from several limitations. First, many methods simply concatenate structural information and association information without fully capturing their intrinsic relationships, leading to suboptimal information fusion. Second, most models lack mechanisms for extracting high-order structural information and aligning heterogeneous and homogeneous features, which limits the model's expressiveness and predictive performance. To address these limitations, we propose TransFusionDR, a framework for drug repositioning via contrastive and high-order feature fusion with transformers. We employ a Graph Transformer to extract deep structural features from drug-drug and disease-disease graphs, and utilize a Heterogeneous Graph Transformer to capture semantic information from the drug-disease association graph. These features are then refined through contrastive learning to enhance semantic consistency and improve information fusion quality. We further introduce a Transformer Encoder to deeply integrate the homogeneous and heterogeneous features by dynamically modeling semantic dependencies, enabling the extraction of high-order interactions and achieving more effective feature alignment. Experimental results on two benchmark datasets demonstrate that the proposed framework significantly improves prediction accuracy and robustness in drug repositioning tasks, outperforming state-of-the-art methods.

Combination drug therapy represents a cornerstone of modern medicine, offering enhanced therapeutic efficacy and reduced drug resistance. Predicting adverse drug-drug inter-action (DDI) events is crucial, and while multi-modal models show significant promise, the effective integration of diverse data sources remains an open challenge. In this work, we introduce MMDDI-GDSS, a novel multi-modal framework for DDI event prediction. Our framework leverages a multi-head attention mechanism to fuse diverse data modalities-including SMILES, protein targets, enzymes, and pharmacological information-into a unified, enhanced molecular attribute graph. To learn robust and expressive drug representations from this complex graph, MMDDI-GDSS employs a graph diffusion process over static subgraphs, generating a powerful and coherent representation for each drug. We benchmark MMDDI-GDSS against several state-of-the-art methods on two widely used DDI event prediction datasets. Experimental results demonstrate that our model consistently outperforms all baselines. Furthermore, comprehensive ablation studies validate the effectiveness of our key components, highlighting the distinct contributions of the attention-based feature integration and the graph diffusion mechanism.

Artificial intelligence techniques play a pivotal role in the accurate identification of drug-drug interaction (DDI) events, thereby informing clinical decisions and treatment regimens. While existing DDI prediction models have made significant progress by leveraging sequence features such as chemical substructures, targets, and enzymes, they often face limitations in integrating and effectively utilizing multi-modal drug representations. To address these limitations, this study proposes a novel multi-modal feature fusion model for DDI event prediction: MMDDI-SSE. Our approach integrates drug sequence modality with DDI graph representations through a novel architecture that employs static subgraph generation to capture structural properties. The model utilizes a graph autoencoder architecture to learn both local and global topological features from these subgraphs, while simultaneously processing diverse sequence-based characteristics including semantically enhanced pharmacodynamic features, chemical substructures, target proteins, and enzyme information. Through comprehensive evaluation on two distinct datasets, MMDDI-SSE demonstrates superior predictive performance compared to state-of-the-art baselines. Ablation studies further validate the effectiveness of each architectural component in enhancing DDI prediction accuracy.

Text classification is a critical task for understanding the knowledge behind text, especially in medical text. In this paper, we propose a medical graph diffusion model, named the MGD model, for the medical text classification task. To model more structural relationships within a document, our MGD model constructs a text heterogeneous graph to represent word-level, sentence-level, and word-sentence-level structural relationships. To overcome the limitation of only considering direct neighbors, a graph diffusion convolution is employed to reconstruct the text heterogeneous graph. Subsequently, a heterogeneous graph convolutional network and a multilayer perceptron are used to complete the medical text classification task. To evaluate the performance of our MGD model, various text classification benchmarks, including long text standard benchmarks, short text standard benchmarks, and medical text benchmarks, are used to comprehensively assess the effectiveness and robustness of our MGD model. Compared with other representative baselines, it achieved notable improvements in both Accuracy and F1 score evaluation metrics. Ablation experiment results further demonstrated that the construction of heterogeneous graphs and the use of diffusion graph convolutional networks significantly impact the performance of our MGD model.

Drug-drug interactions pose a significant challenge in healthcare, directly impacting patient safety and treatment efficacy. Although recent advances in computational methods have improved drug-drug interaction (DDI) event prediction, many existing models face difficulties in effectively fusing features across different DDI tasks. To address these limitations, we introduce MMDDI-MGPFF, a novel multi-modal drug representation learning framework that integrates molecular graphs and pharmacological feature fusion to improve DDI prediction. Our model leverages a graph isomorphism network (GIN) for efficient encoding of molecular structures, coupled with an autoencoder for learning sequence-based drug features, encompassing both biological and pharmacological characteristics. We propose a multi-modal fusion approach that employs multi-head attention and deep neural networks to seamlessly integrate these graph and sequence modalities. Comprehensive experiments on a benchmark dataset demonstrate that MMDDI-MGPFF significantly outperforms state-of-the-art methods in DDI prediction tasks. Ablation studies further corroborate the efficacy of our model’s components, particularly the GIN and the integration of multi-feature drug representations. This work advances the field of DDI prediction by providing a more holistic and accurate approach to drug representation and interaction modeling.

Text classification is a central part of natural language processing, with important applications in understanding the knowledge behind biomedical texts including electronic health records (EHR). In this article, we propose a novel heterogeneous graph convolutional network method for classifying EHR texts. Our method, called EHR-HGCN, is able to combine context-sensitive word and sentence embeddings with structural sentence-level and word-level relation information to perform text classification. EHR-HGCN reframes EHR text classification as a graph classification task to better capture structural information about the document using a heterogeneous graph. To mine contextual information from a document, EHR-HGCN first applies a bidirectional recurrent neural network (BiRNN) on word embeddings obtained via Global Vectors for word representation (GloVe) to obtain context-sensitive word-level and sentence-level embeddings. To mine structural relationships from the document, EHR-HGCN then constructs a heterogeneous graph over the word and sentence embeddings, where sentence-word and word-word relationships are represented by graph edges. Finally, a heterogeneous graph convolutional neural network is used to classify documents by their graph representation. We evaluate EHR-HGCN on a variety of standard text classification benchmarks and find that EHR-HGCN has higher accuracy and F1-score than other representative machine learning and deep learning methods. We also apply EHR-HGCN to the MedLit benchmark and find it performs with high accuracy and F1-score on the task of section classification in EHR texts. Our ablation experiments show that the heterogeneous graph construction and heterogeneous graph convolutional network are critical to the performance of EHR-HGCN.

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy.

BACKGROUND: Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction. As many different types of relationships exist between drug and target, drug-target interaction network can be used for modeling drug-target interaction relationship. Recent works on drug-target interaction network are mostly concentrate on drug node or target node and neglecting the relationships between drug-target. RESULTS: We propose a novel prediction method for modeling the relationship between drug and target independently. Firstly, we use different level relationships of drugs and targets to construct feature of drug-target interaction. Then, we use line graph to model drug-target interaction. After that, we introduce graph transformer network to predict drug-target interaction. CONCLUSIONS: This method introduces a line graph to model the relationship between drug and target. After transforming drug-target interactions from links to nodes, a graph transformer network is used to accomplish the task of predicting drug-target interactions.