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A Graph Neural Network Approach for Predicting the Lung Carcinogenicity of Single Molecular Compounds
http://doi.org/10.5626/JOK.2025.52.6.482
Cancer is one of the major diseases causing millions of deaths worldwide every year, and lung cancer has been recorded as the leading cause of cancer-related deaths in Korea in 2022. Therefore, research on lung cancer-causing compounds is essential, and this study proposes and evaluates a novel approach to predict lung cancer-causing potential using graph neural networks to overcome the limitations of existing machine learning and deep learning methods. Based on SMILES(Simplified Molecular Input Line Entry System) information from the compound carcinogenicity databases CPDB, CCRIS, IRIS and T3DB, the structure and chemical properties of molecules were converted into graph data for training, and the proposed model showed superior prediction performance compared to other models. This demonstrates the potential of graph neural networks as an effective tool for lung cancer prediction and suggests that they can make important contributions to future cancer research and treatment development.
Drug-Drug Interaction Prediction Model Based on Deep Learning Using Drug Information Document Embedding
http://doi.org/10.5626/JOK.2024.51.6.503
The use of polypharmacy has emerged as a promising approach for various diseases, including cancer, hypertension, and asthma. However, the use of polypharmacy can result in unexpected interactions, which may lead to adverse drug effects. Therefore, predicting drug-drug interactions (DDI) is essential for safe medication practices. In this study, we propose a drug-drug interaction prediction model based on deep learning using document embedding to represent the drug. We generate documents about drug information by combining DrugBank data, which includes drug descriptions, indications, mechanisms of action, pharmacodynamics, and toxicity. Then we use Doc2Vec and BioSentVec language models to generate drug representation vectors from the drug information documents. The two drug vectors are paired and input into the deep learning-based prediction model, which outputs the likelihood of interaction between the two drugs. Our goal is to construct the optimal model for predicting drug-drug interactions by comparing the performance under various conditions, including language embedding model performance and adjustments for data imbalance. We expect the proposed model to be utilized for the advanced prediction of drug interactions during the drug prescription process and the clinical stages of new drug development.
Machine Learning-Based Approach for Predicting Drug-Induced Liver Injury of Chemical Compounds
http://doi.org/10.5626/JOK.2023.50.9.777
Drug-induced liver injury (DILI) is one of the factors constraining the distribution of investigational products on the market. Therefore, DILI risk of compounds should be assessed in advance. Although in vivo and in vitro methods can be used to test drug safety, both methods are labor-intensive, time consuming and expensive. In this study, we suggested random forest, light gradient boosting machine, logistic regression models to overcome the above problems. These models used molecular structure and physicochemical features as input to predict the DILI as output. The optimal model was random forest, which performed well for evaluation metrics overall. The proposed model is expected to help drug development process by identifying potential DILI of drug candidates in advance.
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