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Protein-Ligand Binding Affinity Prediction Using Protein Modality Alignme
http://doi.org/10.5626/JOK.2025.52.5.415
Identifying molecules with high binding affinity to a target protein for drug candidate discovery requires significant resources and time. Deep learning-based protein-ligand binding affinity prediction research plays a crucial role in addressing this challenge. Existing studies have utilized protein sequence and structural information along with ligand 2D structures. However, they have limitations in fully capturing complex interactions. Additionally, while sequence, structure, and surface information are used for protein modeling, previous approaches have struggled to incorporate their dependent relationships into the model. In this paper, we proposed a model that could inject these dependencies by aligning protein sequence, structure, and surface information based on sequence data. Furthermore, our model leverages both 2D structure of the ligand and its 3D representation using an SE(3)-invariant graph neural network. The proposed model outperformed existing baseline models. An ablation study demonstrated the importance of aligning different protein modalities and incorporating both 2D and 3D ligand information.
Drug Toxicity Prediction Using Integrated Graph Neural Networks and Attention-Based Random Walk Algorithm
Jong-Hoon Park, Jae-Woo Chu, Young-Rae Cho
http://doi.org/10.5626/JOK.2025.52.3.234
The traditional drug development process is often burdened by high costs and lengthy timelines, leading to increasing interest in AI-based drug development. In particular, the importance of AI models for preemptively evaluating drug toxicity is being emphasized. In this study, we propose a novel drug toxicity prediction model, named Integrated GNNs and Attention Randon Walk (IG-ARW). The proposed method integrates various Graph Neural Network (GNN) models and uses attention mechanisms to compute random walk transition probabilities, extracting graph features precisely. The model then conducts random walks to extract node features and graph features, ultimately predicting drug toxicity. IG-ARW was evaluated on three different datasets, demonstrating strong performances with AUC scores of 0.8315, 0.8894, and 0.7476, respectively. Notably, the model was proven to be highly effective not only in toxicity prediction, but also in predicting other drug characteristics.
HAGCN: Heterogeneous Attentive GCN for Gene-Disease Association
http://doi.org/10.5626/JOK.2025.52.2.161
Predicting gene-disease associations (GDAs) is essential for understanding molecular mechanisms, diagnosing disease, and targeting genes. Validating causal relationships between diseases and genes using experimental methods can be extremely costly and time-consuming. Deep learning, particularly graph neural networks, has shown great promise in this area. However, most models rely on single-source, homogeneous graphs. Another is the need for expert knowledge in manual definition of meta-paths to build multi-source heterogeneous graphs. Recognizing these challenges, the present study introduces the Heterogeneous Attentive Graph Convolution Network (HAGCN). HAGCN processes heterogeneous biological entity association graphs as input. We construct the input graphs using the biological association information from curated databases such as Gene Ontology, Disease Ontology, Human Phenotype Ontology, and TBGA. HAGCN learns the relationship heterogeneity between biological entities without meta-paths by using the attention mechanism. HAGCN achieved the best performance in AUC-ROC in a binary classification task to predict gene-disease association, and also achieved competitive performance in F1 score, MCC, and accuracy against baselines. We believe that HAGCN can accelerate the discovery of disease-associated genes and
An Effective Graph Edit Distance Model Using Node Mapping Information
http://doi.org/10.5626/JOK.2025.52.1.88
Graph Edit Distance (GED) is the most representative method for quantifying similarity between graphs. However, calculating an exact GED is an NP-Hard problem, which incurs a prohibitively large amount of computational cost. To efficiently compute GED, recent studies have focused on deriving an approximate GED between graphs using deep learning models. However, existing models tend to exhibit large approximation errors and suffer from insufficient interpretability because they do not consider node-to-node relationships between graphs. To remedy these problems faced by existing models, a model that could learn a mapping matrix through node-level embeddings of two graphs was proposed in this study to provide better interpretability of the GED approximation while minimizing information loss during the learning process. Results of experiments showed that the proposed model consistently outperformed existing models.
Photovoltaic Power Forecasting Scheme Based on Graph Neural Networks through Long- and Short-Term Time Pattern Learning
Jaeseung Lee, Sungwoo Park, Jaeuk Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2024.51.8.690
As the use of solar energy has become increasingly common in recent years, there has been active research in predicting the amount of photovoltaic power generation to improve the efficiency of solar energy. In this context, photovoltaic power forecasting models based on graph neural networks have been presented, going beyond existing deep learning models. These models enhance prediction accuracy by learning the interactions between regions. Specifically, they consider how the amount of photovoltaic power in a specific region is affected by the climate conditions of adjacent regions and the time pattern of photovoltaic power generation. However, existing models mainly rely on a fixed graph structure, making it difficult to capture temporal and spatial interactions. In this paper, we propose a graph neural networks-based photovoltaic power forecasting scheme that takes into account both long-term and short-term time patterns of regional photovoltaic power generation data. We then incorporate these patterns into the learning process to establish correlations between regions. Compared to other graph neural networks-based prediction models, our proposed scheme achieved a performance improvement of up to 7.49% based on the RRSE, demonstrating its superiority.
Improving Prediction of Chronic Hepatitis B Treatment Response Using Molecular Embedding
Jihyeon Song, Soon Sun Kim, Ji Eun Han, Hyo Jung Cho, Jae Youn Cheong, Charmgil Hong
http://doi.org/10.5626/JOK.2024.51.7.627
Chronic hepatitis B patients with no timely treatment are at a high risk of developing complications such as liver cirrhosis and hepatocellular carcinoma (liver cancer). As a result, various antiviral agents for hepatitis B have been developed, and due to the different components of these antiviral agents, there can be variations in treatment responses among patients. Therefore, selecting the appropriate medication that leads to a favorable treatment response is considered crucial. In this study, in addition to the patient's blood test results and electronic medical records indicating drug prescriptions, information about components of the hepatitis B antiviral agents was incorporated for learning. The aim was to enhance the prediction performance of treatment responses one year after chronic hepatitis B patients' treatment. Molecular embedding of the antiviral agents included both fixed molecular embedding and those generated through an end-to-end structure utilizing a graph neural network model. By comparing with the baseline model, drug molecule embedding was confirmed to contribute to improving performance.
Graph Structure Learning-Based Neural Network for ETF Price Movement Prediction
Hyeonsoo Jo, Jin-gee Kim, Taehun Kim, Kijung Shin
http://doi.org/10.5626/JOK.2024.51.5.473
Exchange-Traded Funds (ETFs) are index funds that mirror particular market indices, usually associated with their low risk and expense ratio to individual investors. Various methods have emerged for accurately predicting ETF price movements, and recently, AI-based technologies have been developed. One representative method involves using time-series-based neural networks to predict the price movement of ETFs. This approach effectively incorporates past price information of ETFs, allowing the prediction of their movement. However, it has a limitation as it only utilizes historical information of individual ETFs and does not account for the relationships and interactions between different ETFs. To address this issue, we propose a model that can capture relationships between ETFs. The proposed model uses graph structure learning to infer a graph representing relationships between ETFs. Based on this, a graph neural network predicts the ETF price movement. The proposed model demonstrates superior performance compared to time-series-based deep-learning models that only use individual ETF information.
Graph Structure Learning: Reflecting Types of Relationships between Sensors in Multivariate Time Series Anomaly Detection
http://doi.org/10.5626/JOK.2024.51.3.236
Sensors are used to monitor systems in various fields, such as water treatment systems and smart factories. Anomalies in the system can be detected by analyzing multivariate time series consisting of sensor data. To efficiently detect anomalies, information about the relationships between sensors is required, but this information is generally difficult to obtain. To solve this problem, the previous work used sensor data to identify relationships between sensors, which were then represented using a graph structure. However, in this process, the graph structure only reflects the presence of relationships between sensors, not the types of relationships between sensors. In this pap er, we considered the types of relationships between sensors in graph structure learning and analyzed multivariate time series to detect anomalies in the system. Experiments show that improving detection accuracy in graph structure learning for multivariate time series anomaly detection involves taking into account the different kinds of relationships among sensors.
Explainable Artificial Intelligence in Molecular Graph Classification
Yeongyeong Son, Yewon Shin, Sunyoung Kwon
http://doi.org/10.5626/JOK.2024.51.2.157
With the advancement of artificial intelligence (AI), there is a growing need for explainable artificial intelligence (XAI). Recently, Graph neural network-based XAI research has been actively conducted, but it mainly focuses on generic graphs. Due to the distinctive characteristics relying on the chemical properties of molecular graphs, we emphasize the necessity for research to investigate whether existing XAI techniques can provide interpretability in molecular graphs. In this paper, we employ existing XAI techniques to molecular graphs and assess them quantitatively and qualitatively to see their interpretability. Furthermore, we examine the outcomes after standardizing the significance ratio of essential features, highlighting the significance of sparsity as one of the XAI evaluation metrics.
Deep k-Means Node Clustering Based on Graph Neural Networks
http://doi.org/10.5626/JOK.2023.50.12.1153
Recently, graph node clustering techniques using graph neural networks (GNNs) have been actively studied. Notably, most of these studies use a GNN to embed each node into a low-dimensional vector and then cluster the embedding vectors using the existing clustering algorithms. However, since this approach does not consider the final goal of clustering when training the GNN, it is difficult to say that it produces optimal clustering results. Therefore, in this paper, we propose a deep k-means clustering method that iteratively trains a GNN considering the final goal of k-means clustering and performs k-means clustering on the embedding vectors generated by the trained GNN. The proposed method considers both the similarity between nodes and the loss of k-means clustering when training a GNN. Experimental results using real datasets confirmed that the proposed method improves the quality of k-means clustering results compared to the existing methods.
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