Network Graph Research Articles

Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.

Syntactic–Semantic Detection of Clone-Caused Vulnerabilities in the IoT Devices

This paper addresses the problem of IoT security caused by code cloning when developing a massive variety of different smart devices. A clone detection method is proposed to identify clone-caused vulnerabilities in IoT software. A hybrid solution combines syntactic and semantic analyses of the code. Based on the recovered code, an attributed abstract syntax tree is constructed for each code fragment. All nodes of the commonly used abstract syntax tree are proposed to be weighted with semantic attribute vectors. Each attributed tree is then encoded as a semantic vector using a Deep Graph Neural Network. Two graph networks are combined into a Siamese neural model, allowing training to generate semantic vectors and compare vector pairs within each training epoch. Semantic analysis is also applied to clones with low similarity metric values. This allows one to correct the similarity decision in the case of incorrect matching of functions at the syntactic level. To automate the search for clones, the BinDiff algorithm is added in the first stage to accurately select clone candidates. This has a positive impact on the ability to apply the proposed method to large sets of binary code. In an experimental study, the developed method—compared to BinDiff, Gemini, and Asteria tools—has demonstrated the highest efficiency.

NIMG-50. MULTIPARAMETRIC MRI DISTINGUISHES NEURONAL AND IMMUNE SIGNATURES IN INVASIVE HIGH-GRADE GLIOMA RIM THROUGH GRAPH NETWORK ANALYSIS

Abstract BACKGROUND Standard of care surgery for high grade glioma (HGG) employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, leaving a non-enhancing (NE) invasive rim of tumor which contributes to recurrence. Multi-parametric MRI (mpMRI) has recently been used to map regional biological heterogeneity of neuronal and immune signatures in HGG, and continues to offer a promising strategy for non-invasive surveillance of tumor progression. Here, we present a graph network analysis relating mpMRI to biological signatures in the NE tumor rim and show the utility of communities of imaging features for discriminating tumor phenotypes. METHODS 53 mpMRI features across 101 spatially-localized RNA-sequenced biopsy samples from 64 patients with IDH-wt HGG were represented as nodes within a Neo4j graph network. Modularity optimization was used to identify communities of densely connected nodes. Communities of nodes representing high proportions of NE samples (one-tailed test) were kept for further analysis. Gene set enrichment analysis (GSEA) on samples within selected imaging communities was analyzed for biological process enrichment using clusterProfiler. Single sample GSEA (ssGSEA) using ssGSEA2 was used to annotate neuronal and immune cell states across samples within selected communities. mpMRI feature selection within each community identified the most relevant imaging features for isolating samples classified into neuronal and immune cell states. RESULTS 11 imaging communities gathered a high proportion of NE samples. Three NE communities showed significant enrichment of neuronal signatures, while one showed significant enrichment of immune pathways. One neuronal-predominant community showed unique upregulation of 14 pathways related to neuronal migration/astrocyte projection. CONCLUSION Network imaging communities can identify clusters of NE glioma samples showing contrasting neuronal and immune signatures, including distinct upregulation of migration and projection pathways. Mapping these imaging feature communities to specific biological processes implicated in glioma progression may aid treatment planning and prognostication for glioma patients.

ANGI-10. NETWORK ANALYSIS ESTABLISHES CSF2RA’S REGIONAL INFLUENCE ON HYPOXIA RESPONSE IN THE INVASIVE RIM OF HIGH GRADE GLIOMA

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer. Standard of care surgery employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, leaving a non-enhancing (NE) invasive rim of tumor which contributes to recurrence. The hypoxic tumor microenvironment is thought to drive invasion into healthy brain, and understanding cellular responses to hypoxia may provide insight into combatting recurrence. Here, we apply a graph network to integrate regionally heterogeneous data from a unique dataset of spatially-localized CE and NE biopsies from a large cohort of patients with HGG to identify gene networks regulating hypoxia response and invasion. METHODS RNA-sequencing of 1159 differentially expressed genes across 159 IDH-wt biopsies (74 patients) from NE/CE regions were represented as nodes in a Neo4j graph network. Candidate genes for network random walks occurred exclusively within NE communities detected by modularity optimization and were localized by cell-type using single-cell RNA-sequencing (scRNAseq). CSF2RA was selected as source (walk length = 5; 10,000 walks/source node). Frequently occurring genes (one-tailed test) in walks were ranked by correlation with CSF2RA expression in NE samples and analyzed by gene set enrichment analysis for biological processes (clusterProfiler). CSF2RA and hypoxia gene expression were visualized with DoHeatmap in composite NE/CE scRNAseq profiles. RESULTS CSF2RA, an immune receptor, was upregulated (logFC = 0.789) in NE samples, scRNAseq myeloid populations, and Glycolytic/Plurimetabolic glioma subtype. CSF2RA random walk genes (n=46) enriched metabolic/proliferative signatures. CSF2RA expression correlated negatively with hypoxia genes globally and regionally (NE/CE), while NE/CE scRNAseq revealed distinct cellular expression patterns of CSF2RA and hypoxia response genes. CONCLUSION CSF2RA expression may prime NE glioma cells for invasion by altering hypoxia response. The genetic network bridging CSF2RA and hypoxia response may be a targetable mechanism of tumor recurrence.

ANGI-09. GRAPH NETWORK ANALYSIS IDENTIFIES GENETIC COMMUNITIES OF CSF2RA AND RBFOX1 AS REGULATORS OF INVASIVE HIGH-GRADE GLIOMA BIOLOGY

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer. Standard of care surgery employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, leaving a non-enhancing (NE) invasive rim of tumor which contributes to recurrence. Recent multi-omics analyses identified predominant neuronal and immune signatures within NE tumor. Here, we apply a graph network to integrate regionally heterogeneous data from a unique dataset of spatially-localized CE and NE biopsies from a large cohort of patients with HGG. METHODS RNA-sequencing of 1159 differentially expressed genes across 159 IDH-wt biopsies (74 patients) from NE/CE regions were represented as nodes in a Neo4j network. Candidate genes for network random walks occurred exclusively within NE communities detected by modularity optimization and localized by cell-type using single-cell RNA-sequencing (scRNAseq). CSF2RA and RBFOX1 were selected as sources (walk length = 5; 10,000 walks/source node). Frequently occurring genes (one-tailed test) in walks were ranked by correlation with CSF2RA/RBFOX1 expression in NE samples and analyzed by gene set enrichment analysis for biological processes (clusterProfiler). Random walk genes were visualized (DoHeatmap) against composite NE/CE scRNAseq profiles. RESULTS CSF2RA and RBFOX1 were upregulated in NE samples (logFC = 0.789, 1.85). Upregulation of CSF2RA, an immune receptor, in the Glycolytic/Plurimetabolic and RBFOX1, a neurodevelopmental regulator, in the Neuronal glioma subtypes were observed in NE samples (ANOVA p < 0.05). CSF2RA walk genes (n=46) enriched metabolic/proliferative signatures, while RBFOX1 walk genes (n=201) showed neurodevelopmental upregulation. scRNAseq localized CSF2RA to monocytes/macrophages while RBFOX1 was expressed in distinct astrocyte/neuroepithelial populations. CONCLUSION CSF2RA and RBFOX1’s gene networks modulate immune and neurodevelopmental processes influencing glioma progression in NE tumor. Understanding the region-specific roles of these networks may offer strategies for targeting cells in the invasive glioma rim.

Triangle-induced and degree-wise sampling over large graphs in social networks

Triangle-induced and degree-wise sampling over large graphs in social networks

Global research hotspots and trends of Buyang huanwu decoction: A visual analysis of the literature based on CiteSpace.

Buyang huanwu decoction (BYHWD) has shown significant clinical efficacy in the treatment of several diseases, particularly stroke. However, bibliometric research has not been comprehensive. BYHWD articles were collected from literature databases published from January 1, 1915, to March 31, 2024, including the China National Knowledge Infrastructure, Weipu, Wanfang, Pubmed, Scopus, and Web of Science Core Collection. Knowledge network graphs of annual publication volume, authors, institutions, countries, keywords, and references were constructed. Nine thousand two hundred thirty-eight Chinese literature and 559 English articles published between 1915 and 2024 showed an overall upward trend. The countries, institutions, journals, and authors with the highest output were China, Hunan University of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangxian Cai, and Changqing Deng, respectively. Research teams outside China were located at Daejeon University, Gachon University, Aga Khan University, Yale University, etc. The results of keyword co-occurrence and burst analysis included clinical applications, animal experiments, action mechanisms, clinical efficacy, and safety evaluations based on systematic reviews and meta-analyses. Literature co-citation analysis revealed that BYHWD was highly correlated with neuroprotection and reduction of cerebral ischemia/reperfusion injury. Both Chinese and English literature have shown overall growth trend since 1984 and 1989, respectively. Clinical applications, pharmacological effects, mechanisms, active ingredients, evaluation of clinical efficacy and safety, modified BYHWD, methods, and biological techniques may be hotspots and focus of future research on BYHWD. Hotspot analytical methods and biological techniques include systematic reviews, meta-analyses, data mining, network pharmacology, and molecular docking. Future valuable research fields may include studies on neuroprotection, anti-inflammatory activity, ischemic stroke, bioactive compounds, and their underlying mechanisms.

Defense mechanisms in individuals with depressive and anxiety symptoms: a network analysis

BackgroundDefense mechanisms play a crucial role in depression and anxiety. The current study aimed at estimating the network structure of defense mechanisms in individuals with symptoms of depression and anxiety to understand the most central defenses and relevant connections. Moreover, we aimed at examining the associations between defense mechanisms and symptoms.MethodsWe employed the Symptom Checklist-90 to recruit 655 individuals with depressive and anxiety symptoms during the first wave of the COVID-2019 Pandemic in Italy. Defense mechanisms were assessed with the DMRS-SR-30.ResultsResults showed a main component in the network graph featuring 16 defense mechanisms. Self-assertion was the most central node in the network, displaying positive and negative connections with an array of mature and immature defenses, respectively. Among immature defenses, passive aggression was the most interconnected node. Some mature defenses (i.e., humor, affiliation, and sublimation) were not connected to other nodes. A range of defense mechanisms were associated with anxiety and depressive symptoms.ConclusionsThis is the first research effort supporting the conceptualization of defense mechanisms as a complex system. Results suggest that defense mechanisms of the same cluster (e.g., mature defenses) play different roles in the network. Central defenses (i.e., self-assertion and passive aggression) detected in this study may be promising intervention targets.

A Holistic Network Analysis of the Money Laundering Threat Landscape: Assessing Criminal Typologies, Resilience and Implications for Disruption

Abstract Objective This article visualises money laundering, a crime spanning hundreds of different actors, methods and value instruments, as a network. Possible combinations of money laundering actions and schemes are analysed through a holistic visualisation to draw policy-relevant insights into their prevention. Methods A prior scoping review of money laundering typologies identified 793 activities that a money launderer can take to obfuscate illicit funds. These actions form the basis of a directed network graph showcasing all possible combinations of money laundering schemes. Three network-based analyses are then conducted: (1) centrality analysis, to determine the most ‘important’ money laundering actions as priorities for prevention, (2) resilience analysis to simulate iterative interventions against different money laundering actions to assess their impact on the wider network and (3) subgroup analysis to identify groups of commonly undertaken schemes (i.e. “typologies”). Results Money laundering is found to be a highly resilient process crime, with specific interventions often unable to prevent schemes from displacing to alternative activities to launder their funds. However, benefits of tactically displacing criminals to more easily detectable schemes are discussed. The formulation of money laundering typologies through more empirically robust processes is also motivated, in place of compartmentalised reports that remain the current standard practice. Conclusions Network analysis is motivated as an effective way of visualising complex process crimes with vast criminal opportunities such as money laundering, allowing policy-relevant insights to be drawn in terms of resource-allocation, strategy and prioritisation. Theoretical and policy implications of the current study are discussed in the context of crime scientific theories.

Towards Integrating Federated Learning with Split Learning via Spatio-temporal Graph Framework for Brain Disease Prediction.

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the other hand, integrating data from multiple sites is challenging due to data privacy restrictions. To address these issues, we propose a novel, integrated Federated learning and Split learning Spatio-temporal Graph framework (FS2G). Specifically, we introduce federated learning and split learning techniques to split a spatio-temporal model into a client temporal model and a server spatial model. In the client temporal model, we propose a time-aware mechanism to focus on changes in brain functional states and use an InceptionTime model to extract information about changes in the brain states of each subject. In the server spatial model, we propose a united graph convolutional network to integrate multiple graph convolutional networks. Integrating federated learning and split learning, FS2G can utilize multi-site fMRI data without violating data privacy protection and reduce the risk of overfitting as it is capable of learning from limited training data sets. Moreover, it boosts the extraction of spatio-temporal features of fMRI using spatio-temporal graph networks. Experiments on ABIDE and ADHD200 datasets demonstrate that our proposed method outperforms state-of-the-art methods. In addition, we explore biomarkers associated with brain disease prediction using community discovery algorithms using intermediate results of FS2G. The source code is available at https://github.com/yutian0315/FS2G.

Revealing the potential causality of suicidal ideation and psychopathic symptoms among survivors 10 years after the Wenchuan earthquake using network analysis.

Little is known about the interactions and causal inference between psychopathic symptoms and suicidal ideation (SI) among earthquake survivors. Using a 10-year large cohort of Chinese adolescents exposed to the Wenchuan earthquake, this study aimed to structure networks of suicidal ideation and some specific symptoms of posttraumatic symptoms and depression among survivors of the Wenchuan earthquake to clarify the interactions and causality between these symptoms. A total of 684 survivors who completed surveys at 6 months and 10 years postearthquake and had one or above psychopathic symptoms were included in the analyses. Sociodemographic, posttraumatic stress symptoms, depressive symptoms, and suicidal ideation were measured at 6 months and/or 10 years postearthquake. Network analyses were performed based on R Version 4.1.3. The Gaussian graphical model network revealed that "depressed mood" had the highest expected influence value. In addition, the directed acyclic graph network revealed that anhedonia could affect "depressed mood," which further leads to "suicidal ideation." The pathway of "anhedonia → depressed mood → suicidal ideation" was a crucial psychopathic mechanism of suicidal ideation. It informs that intervening in anhedonia and depressed mood among earthquake-exposed survivors is necessary to potentially decrease the risk of future suicidal ideation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Data-driven multivariate time series prediction of in-vehicle equipment failure rates

Effectively predicting the failure rate of train-controlled on-board equipment is of great significance for rationally allocating equipment spares, drawing up maintenance plans, and reducing the occurrence of failures. In order to tackle the problem of the intricate attributes and limited predictive precision of the sample data pertaining to the failure rate of on-board equipment, in this paper, a multivariate time series prediction model for the failure rate of train-controlled on-board equipment is based on the combination of multivariate variational modal decomposition (MVMD) graph neural network (GNN) and transformer. First, the original failure rate time series, air temperature, humidity and sand and dust time series are modally decomposed using MVMD, and the intrinsic modal functions (IMFs) of each series are obtained; then, the dynamic graph of the GNN network is defined according to the IMFs, and Transformer’s self-attention mechanism is utilised to capture the dynamic graph’s temporal and spatial dependencies. Finally, the failure rate is output through the feed-forward neural network prediction value. Experiments using the historical fault data of CTCS3-300T train-controlled on-board equipment are carried out to confirm the efficacy of the suggested method, and it is contrasted with other conventional machine learning techniques. The outcomes of the experiment show that compared with other train-controlled on-board equipment failure prediction models, the proposed method has a very good superiority, as evidenced by its mean absolute error (MAE) of 0.0489 and root mean square error (RMSE) of 0.0510, which is of certain reference value for equipment operation and maintenance.

The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.

Continuous-Time Dynamic Graph Networks Integrated with Knowledge Propagation for Social Media Rumor Detection

The proliferation of the Internet and mobile devices has made it increasingly easy to propagate rumors on social media. Widespread rumors can incite public panic and have detrimental effects on individuals. In recent years, researchers have found that both the spatial structure of rumor diffusion and the temporal features of propagation can be effective in identifying rumors. However, existing methods tend to focus on either spatial structure or temporal information in isolation, and few models can effectively capture both types of information. Additionally, most existing methods treat continuously changing temporal information as static snapshots, neglecting the precise timing of propagation. Moreover, as users repost and comment, background knowledge associated with the posts also evolves dynamically, which is often ignored. To address these limitations, we propose CGNKP (Continuous-time Dynamic Graph Networks integrated with Knowledge Propagation), a model that jointly captures the spatial structure and continuous-time features of post propagation to fully understand the dynamics of background knowledge. Specifically, we introduce a novel method for encoding continuous-time dynamic graphs, modeling the propagation process through two dynamic graphs: a temporal propagation graph (for posts diffusion) and a temporal knowledge graph (for knowledge diffusion). Extensive experiments on real-world datasets demonstrate that CGNKP significantly outperforms multiple strong baselines, achieving accuracies of 0.861 on the Twitter15 dataset and 0.903 on the Twitter16 dataset.

Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient

In the current age of chemical science, chemical graph theory has significantly advanced our understanding of the characteristics of chemical compounds. To simulate the mathematical, chemical, and physical aspects of networks, a topological index, a numerical measure obtained from the graph of a chemical network, employed. Recent work has explored the topological properties of boron oxide using chemical graph theory. In this work, we conduct a Pearson correlation analysis of boron oxide to assess the correlations between the Van and S indices and entropy metrics. We analyze the Pearson correlation coefficients between the entropy values and the calculated indices using a heatmap. In this article, a significant positive correlation between the Van, and S indices, and entropy values, which is represented by the heatmap of the strong linear correlations. To avoid duplication, a dimensionality reduction technique should be used for highly connected variables. Additionally, this study gives a detailed explanation of the link between the indices and entropy, which will form the basis of further statistical investigations.

A Text Generation Method Based on a Multimodal Knowledge Graph for Fault Diagnosis of Consumer Electronics

As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics promptly and accurately, we propose a multimodal knowledge graph-based text generation method. Our method begins by using deep learning models like the Residual Network (ResNet) and Bidirectional Encoder Representations from Transformers (BERT) to extract features from user-provided fault information, which can include images, text, audio, and even olfactory data. These multimodal features are then combined to form a comprehensive representation. The fused features are fed into a graph convolutional network (GCN) for fault inference, identifying potential fault nodes in the electronics. These fault nodes are subsequently fed into a pre-constructed knowledge graph to determine the final diagnosis. Finally, this information is processed through the Bias-term Fine-tuning (BitFit) enhanced Chinese Pre-trained Transformer (CPT) model, which generates the final fault diagnosis text for the user. The experimental results show that our proposed method achieves a 4.4% improvement over baseline methods, reaching a fault diagnosis accuracy of 98.4%. Our approach effectively leverages multimodal fault information, addressing the challenges users face in diagnosing faults through the integration of graph convolutional network and knowledge graph technologies.

Roaen: reversed dependency graph and orthogonal-gating strategy attention-enhanced network for aspect-level sentiment classification

Roaen: reversed dependency graph and orthogonal-gating strategy attention-enhanced network for aspect-level sentiment classification

Retraction Note: Intelligent recommendation method integrating knowledge graph and bayesian network

Retraction Note: Intelligent recommendation method integrating knowledge graph and bayesian network

Intricate Supply Chain Demand Forecasting Based on Graph Convolution Network

Globalization has contributed to the increasing complexity of supply chain structures. In this regard, precise demand forecasting for the intricate supply chain holds paramount importance in effective supply chain management. Traditional statistical models and deep learning methods often face challenges in efficiently discerning correlations within a myriad of interconnected demands. To tackle this issue, this paper proposes an intricate supply chain demand forecasting method based on graph convolution networks adept at handling non-Euclidean data. First, the companies within the supply chain are treated as nodes in the graph structure, and the relationships between them are treated as edges, with demand data serving as the features of these edges. Then, a graph convolutional network is constructed to aggregate node and edge information. Through a multi-layer network, the relationships among nodes, edges, and historical demand are established to facilitate the prediction of supply chain demands. In this process, the graph convolutional network incorporates supply chain connectivity information into demand time series analysis. This integration of surface-level topological features and deeper latent correlation attributes across the supply chain’s nodes refines the demand forecasting precision across the entire supply chain. The validation experiment in this paper is grounded in sales data of a singular product from multiple sales nodes of an electronics company. The results demonstrate that the proposed method surpasses four other traditional demand forecasting algorithms significantly in terms of accuracy, providing substantial evidence for the superior performance of graph networks in the analysis of intricate supply chain relationships.

Unraveling the impact of mutations on the functional dynamics, stability, and energetics of dengue virus non-structural protein 1

ABSTRACT The non-structural protein 1 (NS1), a viral toxin, is linked to serious consequences of dengue. Given its crucial role in the viral life cycle, NS1 has been a primary target for antiviral drug development. This study elucidates the mechanistic impact of specific NS1 mutations, V220D and A248V, identified by Tan and colleagues as potential inhibitors of viral replication. We developed an integrated computational framework to analyze these mutations relative to the native NS1 protein. Using conventional all-atom molecular dynamic simulations followed by site-directed in silico mutagenesis, significant alterations in protein structure were observed. Conformational ensembles over time showed average backbone deviations of 0.44 nm and 0.43 nm for V220D and A248V, respectively, compared to 0.37 nm for the wild type. Residue-wise fluctuations were higher in A248V compared to both wild type and V220D systems. Mutation-induced disruptions in the hydrogen bond network and altered protein structure graphs were also revealed. Principal component analysis and free energy landscape assessments showed the wild-type protein had a more constrained and stable conformation, whereas the mutants displayed scattered energy contours, suggesting reduced structural stability. This comprehensive assessment provides deep insight into the structural impact of NS1 mutations, informing future efforts in antiviral drug development targeting NS1.