Blueprint for Drug Repurposing Success: Foundational Concepts and Practical Framework.

Multitype view of knowledge contrastive learning for recommendation

The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Recent studies have shown that the problems of cold processing and data sparsity in recommender systems can be well alleviated with the help of knowledge graph information. Meanwhile, since the information in recommender systems can be represented by graph data structure, graph neural network has great advantages in recommendatory system. Therefore, graph neural network can better mine the deep relationship between objects in the recommender systems which combines with information from knowledge graph. This paper reviews the application of graph neural network in knowledge graph aided recommendation systems. Specifically, this paper firstly sorts out the related knowledge of knowledge graph, recommendation system and graph neural network, including the cold processing and data sparsity, the structure of knowledge graph, and the basic modules of graph neural network. This paper also summarizes the methods such as KGAT and CKAN used in knowledge graph aided recommendation systems in recent years, and gives some discussion on the development of this field.

Traffic forecasting is a crucial aspect of modern Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), playing a vital role in improving the safety and efficiency of daily transportation activities. Despite the valuable contributions of traditional machine learning (ML) models and advanced deep learning (DL) techniques, there persist challenges in capturing the intricate spatial and temporal dependencies inherent in traffic flow. In response to these challenges, we present GRAPHITE, an innovative framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to leverage generative reasoning for efficient traffic management. Our model seamlessly integrates historical traffic volume data collected by road sensors with local spatial information encoded through knowledge graphs (KGs) associated with each sensor. These KGs offer a structured representation of relationships between traffic sensors and points of interest (POIs) in their neighborhood, thereby enhancing the comprehension of the urban context and leading to more accurate traffic predictions. Extensive experiments conducted on diverse datasets underscore the efficacy of GRAPHITE. Notably, we achieved a maximum decrease in RMSE of 31.05% compared to GAN-GRU and a maximum increase in R2 of 8.15% compared to GAN-RNN, positioning GRAPHITE as a standout solution among the current state-of-the-art approaches. Our code is available at: https://github.com/MODAL-UNINA/GRAPHITE.

Drug repurposing (DR) offers a promising alternative to the high cost and low success rate of traditional drug development, especially for complex diseases like Alzheimer’s disease (AD). This study addressed DR for AD from three key angles: (1) demonstrating how disease-specific knowledge graphs can improve DR performance, (2) evaluating the role of large language models (LLMs) in enhancing the usability and efficiency of these graphs, and (3) assessing whether Graph-of-Thoughts (GoT)-enhanced LLMs, when integrated with AD knowledge graphs, can outperform traditional machine learning and LLM-based approaches. We tested five distinct DR strategies (DR1–DR5) for AD: DR1, a machine learning method using TxGNN; DR2, a machine learning model leveraging the Alzheimer’s KnowledgeBase (AlzKB); DR3, an LLM-based chatbot built on AlzKB; DR4, our ESCARGOT framework combining GoT-enhanced LLMs with AlzKB; and DR5, a general reasoning-driven LLM approach. Results showed that AlzKB significantly improved DR outcomes. ESCARGOT further enhanced performance while reducing the need for coding or advanced expertise in knowledge graph analysis. Because the architecture of AlzKB is easily adaptable to other diseases and ESCARGOT can integrate with various knowledge graph platforms, this framework offers a broadly applicable, innovative tool for accelerating drug discovery through repurposing.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13040-025-00466-5.

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning.

Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effect for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and Gene Expression Omnibus expression profiles, respectively, then screened drug candidates by a corrected protein-protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data, respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by graph neural networks. An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and DE genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR.

In this study, research has been conducted using pre-trained knowledge graph embedding for drug repurposing in treating ALS (Amyotrophic Lateral Sclerosis), and its results have been presented. Drug repurposing studies for ALS have been carried out through two main methods: disease-drug relationship and genes-drugs relationship. Drug repurposing recommendations for ALS have been provided by predicting connections between disease and drug entities on the DRKG (Drug Repurposing Knowledge Graph). The findings obtained from the study have been evaluated by comparing them with the list of clinical trial drugs obtained from Drugbank. DRKG has been utilized as a comprehensive biological knowledge graph containing genes, compounds, diseases, biological processes, side effects, and symptoms. This graph has proven to be an effective resource for extracting information related to ALS disease. In drug repurposing studies, drugs obtained through disease-drug relationships have been compared with the list of clinical trial drugs associated with ALS, yielding significant results. Additionally, interactions between genes associated with ALS and drugs related to these genes have been examined in studies conducted through gene-drug relationships. The results obtained from the study demonstrate that DRKG is an effective resource for identifying drugs with potential therapeutic effects in the treatment of ALS. The findings suggest that drug repurposing studies could offer new and effective solutions for the treatment of ALS, marking a significant step forward in this regard.

Entity Alignment (EA) aims to identify equivalent entities across different Knowledge Graphs (KGs), enabling knowledge fusion and integration. In recent years, Graph Neural Networks (GNNs) have emerged as a powerful paradigm for EA by leveraging structural information in KGs. However, most existing studies emphasize novel message passing mechanisms while overlooking other crucial GNN design components. This paper presents a comprehensive and systematic evaluation of GNN-based EA methods, focusing on three key aspects: message passing strategies, the number of GNN layers, and the construction of final entity representations. We benchmark a diverse set of GNN models originally developed for tasks such as node classification and knowledge graph completion, and we assess their adaptability to the EA task. Additionally, we explore the effectiveness of skip connection techniques, activation functions, and relational information integration. Our experiments, conducted on standard EA benchmarks including DBP15K and SRPRS, reveal several counterintuitive findings: (1) message passing is indispensable for EA; (2) many node classification GNNs are highly competitive for EA; (3) one or two GNN layers generally achieve optimal performance; and (4) activation functions have minimal impact, while skip connections significantly enhance results. This study provides a principled framework and empirical foundation for designing more effective GNN-based EA models. Code and datasets are publicly available at <uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/kg-bnu/GNN-EA">https://github.com/kg-bnu/GNN-EA</uri>.

The development of entirely new drugs for emerging diseases is challenging, time consuming and very costly. This underscores the importance of drug repurposing (DR), where treatments are found among clinically approved drugs for other diseases. DR gained more popularity with the emergence of the SARS-COV-2 virus. The current pandemic almost halted normal life in most parts of the world. Despite global research efforts on this task, no effective treatment has yet been developed. This research study is an effort to leverage deep learning techniques in network medicine to screen potential therapeutic drugs for COVID-19. We treat drug repurposing as a link prediction task on a large heterogeneous high-quality knowledge graph containing interactions between medical entities like drugs, diseases and genes from various resources. Using Graph Neural Network (GNN) based on GraphSAGE model, we were able to achieve AUC of 0.97. We highlighted the top 5 new drugs for repurposing for COVID-19 including Flurithromycin, Radotinib and Memantine, and predicted 7 drugs currently in clinical trials ranked among the top 100 drugs. The experimental results confirm that the model can effectively predict the drug-disease interaction for COVID-19.KeywordsGraph neural networkKnowledge graphDrug repurposingNetwork medicineCOVID-19 treatment

In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.

Recent computational drug repositioning strategies against SARS-CoV-2

Knowledge Graphs (KGs) have shown to be very important for applications such\nas personal assistants, question-answering systems, and search engines.\nTherefore, it is crucial to ensure their high quality. However, KGs inevitably\ncontain errors, duplicates, and missing values, which may hinder their adoption\nand utility in business applications, as they are not curated, e.g.,\nlow-quality KGs produce low-quality applications that are built on top of them.\nIn this vision paper, we propose a practical knowledge graph curation framework\nfor improving the quality of KGs. First, we define a set of quality metrics for\nassessing the status of KGs, Second, we describe the verification and\nvalidation of KGs as cleaning tasks, Third, we present duplicate detection and\nknowledge fusion strategies for enriching KGs. Furthermore, we give insights\nand directions toward a better architecture for curating KGs.\n

This study proposes an AI-OSINT framework for transnational religious figures, linking knowledge graphs, graph neural networks and Bayesian updating into a computable evidence chain to reconstruct and quantify the overseas assets and foreign contacts of Shi Yongxin and the Shaolin system (2024-11-2025-06). Combining GCN/GAT with unsupervised anomaly detection (Isolation Forest, LOF) on heterogeneous time-series graphs and Bayesian modelling to form monthly outputs of Religious Diplomatic Risk Index (RDRI, 0-100), assessed by ROC-AUC/AUPRC; link prediction by MRR/Hits@k. The results show that the Vatican meeting (2025-02-01) triggered a short-term peak, with the RDRI rising from c. 24-27 to c. 42, and reaching c. 45 in 2025-03; the subsequent chain of "foundations/cultural centres → out-of-country activities" maintained the index at a medium-term high of c. 40 (±3), with a medium-term high of c. 40 (±3), and a medium-term high of c. 40 (±3). The "Foundation/Cultural Centre → Outbound Activities" chain maintains the index at a medium-term high of ~40 (±3), showing a double-engine rhythm of "event amplification + resource penetration". The reported uncertainty interval is ±3; and the conclusion level is shown to be stable under a priori/likelihood ±10% perturbation. The framework enhances the systematic and verifiable study of transnational religious networks without relying on internal intelligence and is transferable to other religious or transnational NGO contexts; to avoid misuse, the RDRI is defined as an early warning scale rather than a factual or judicial characterisation (all judgements cross-checked against registered and chained anchors).Keywords: knowledge graph; graph neural networks; religious diplomacy; financial anomalies; OSINT