• Biomolecules & Therapeutics
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• v.22 no.4
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• pp.267-274
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• 2014

Drug development and preclinical trials are challenging processes and more than 80% to 90% of drug candidates fail to gain approval from the United States Food and Drug Administration. Predictive and efficient tools are required to discover high quality targets and increase the probability of success in the process of new drug development. One such solution to the challenges faced in the development of new drugs and combination therapies is the use of low-cost and experimentally manageable in vivo animal models. Since the 1980's, scientists have been able to genetically modify the mouse genome by removing or replacing a specific gene, which has improved the identification and validation of target genes of interest. Now genetically engineered mouse models (GEMMs) are widely used and have proved to be a powerful tool in drug discovery processes. This review particularly covers recent fascinating technologies for drug discovery and preclinical trials, targeted transgenesis and RNAi mouse, including application and combination of inducible system. Improvements in technologies and the development of new GEMMs are expected to guide future applications of these models to drug discovery and preclinical trials.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1797-1808
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• 2021

Artificial intelligence is considered one of the core technologies leading the 4th industrial revolution. It is adopted in various fields bringing about a huge paradigm shift throughout our society. The field of biotechnology is no exception. It is undergoing innovative development by converging with other disciplines such as computers, electricity, electronics, and so on. In drug discovery and development, big data-based AI technology has a great potential of improving the efficiency and quality of drug development, rapidly advancing to overcome the limitations in the existing drug development process. AI technology is to be specialized and developed for the purpose including clinical efficacy and safety-related end points based on the multidisciplinary knowledge such as biology, chemistry, toxicology, pharmacokinetics, etc. In this paper, we review the current status of AI technology applied for drug discovery and consider its limitations and future direction.

• Korean Journal of Pharmacognosy
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• v.45 no.4
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• pp.354-358
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• 2014

Aldose reductase (AR) has been demonstrated to play important role in the development of the diabetic complications such as diabetic retinopathy, diabetic neuropathy and diabetic nephropathy. To discover novel treatments for diabetic complications from natural sources, 69 Korean herbal medicines have been investigated for inhibitory activities on AR. Among them, 7 herbal medicines, Eleutherococcus sessiliflorus (stems), Artemisia japonica (whole plants), Wisteria floribunda (leaves), Eurya japonica (stems, twigs and leaves, leaves), Ampelopsis brevipedunculata (stems) exhibited a significant inhibitory activity compared with 3,3-tetramethyleneglutaric acid as positive control.

• Journal of Integrative Natural Science
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• v.16 no.4
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• pp.123-131
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• 2023

Breast cancer continues to pose a substantial worldwide health challenge, thereby requiring the development of innovative strategies to discover new therapeutic interventions. Signal Transducer and Activator of Transcription 3 (STAT-3) has been identified as a significant factor in the development of several types of cancer, including breast cancer. This is primarily attributed to its diverse functions in promoting tumour formation and conferring resistance to therapeutic interventions. This study presents an in silico drug repositioning approach that focuses on identifying specific inhibitors of STAT-3 for the purpose of treating breast cancer. We initially examined the structural and functional attributes of STAT-3, thereby elucidating its crucial involvement in cellular signalling cascades. A comprehensive virtual screening was performed on a diverse collection of drugs that have been approved by the FDA from zinc15 database. Various computational techniques, including molecular docking, cross docking, and cDFT analysis, were utilised in order to prioritise potential candidates. This prioritisation was based on their predicted binding energies and outer molecular orbital reactivity. The findings of our study have unveiled a Dihydroergotamine and Paritaprevir that have been approved by the FDA and exhibit considerable promise as selective inhibitors of STAT-3. In conclusion, the utilisation of our in silico drug repositioning approach presents a prompt and economically efficient method for the identification of potential compounds that warrant subsequent experimental validation as selective STAT-3 inhibitors in the context of breast cancer. The present study highlights the considerable potential of employing computational strategies to expedite the drug discovery process. Moreover, it provides valuable insights into novel avenues for targeted therapeutic interventions in the context of breast cancer treatment.

• Korean Journal of Pharmacognosy
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• v.44 no.2
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• pp.161-167
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• 2013

Aldose reductase (AR) has been shown to play an important role in the development of the diabetic complications. To discover novel treatments for diabetic complications from natural sources, 59 Chinese herbal medicines have been investigated for inhibitory activities on AR. Among them, 10 herbal medicines, Catalpa fargesii (stem and leaf), Saussurea Laniceps(whole plant), Alnus nepalensis(stem and leaf), Swertia macrosperma (whole plant), Woodfordia fruticosa (stem and leaf), Elsholtzia bodinieri (whole plant), Elsholtzia fruticosa (whole plant), Rosa multiflora (fruit), Nardostachys chinensis (whole plant), Eurya groffii (stem and leaf) exhibited a significant inhibitory activity compared with 3,3-tetramethyleneglutaric acid (TMG) as positive control. Particularly, 4 herbal medicines, C. fargesii (stem and leaf), S. Laniceps (whole plant), A. nepalensis (stem and leaf), S. macrosperma (whole plant) showed two times more potent inhibitory activity than TMG ($5.37{\mu}g/ml$).

• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Biomolecules & Therapeutics
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• v.26 no.5
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• pp.458-463
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• 2018

The phosphorylation of JNK is known to induce insulin resistance in insulin target tissues. The inhibition of JNK-JIP1 interaction, which interferes JNK phosphorylation, becomes a potential target for drug development of type 2 diabetes. To discover the inhibitors of JNK-JIP1 interaction, we screened out 30 candidates from 4320 compound library with In Cell Interaction Trap method. The candidates were further confirmed and narrowed down to five compounds using the FRET method in a model cell. Among those five compounds, Acebutolol showed notable inhibition of JNK phosphorylation and elevation of glucose uptake in diabetic models of adipocyte and liver cell. Structural computation showed that the binding affinity of Acebutolol on the JNK-JIP1 interaction site was comparable to the known inhibitor, BI-78D3. Our results suggest that Acebutolol, an FDA-approved beta blocker for hypertension therapy, could have a new repurposed effect on type 2 diabetes elevating glucose uptake process by inhibiting JNK-JIP1 interaction.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1326-1334
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• 2019

Knowledge base means a library stored in computer system providing useful information or appropriate solutions to specific area. Knowledge base associated with autism is the complex multidimensional information set related to the disease autism for its pathogenic factor and therapy. This paper focuses on the knowledge of biological molecular information extracted from massive biomedical texts with the aid of widespread used machine learning methods. Six classes of biological molecular information (such as protein, DNA, RNA, cell line, cell component, and cell type) are concerned and the probability statistics method, conditional random fields (CRFs), is utilized to discover these knowledges in this work. The knowledge base can help biologists to etiological analysis and pharmacists to drug development, which can at least answer four questions in question-answering (QA) system, i.e., which proteins are most related to the disease autism, which DNAs play important role to the development of autism, which cell types have the correlation to autism and which cell components participate the process to autism. The work can be visited by the address http://134.175.110.97/bioinfo/index.jsp.

• Journal of the Korean Society of Food Culture
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• v.30 no.5
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• pp.562-575
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• 2015

This study intends to discover stories and sensibilities connected with characteristics and symbols of the history and culture of Sangju to develop contents about the native foods of Sangju. 'General Jeong's Table', which supplied the energy and nutrition necessary for soldiers during war, is set with Jobap, Patipguk, Euneogui, Baechumoojeon, Kongnamulheojib, Patipnamul, and Munamulsileagideanjangmuchim. 'Sangjuseong retaking wartime food', as a kind of ready-to-eat meal, which stresses convenience above everything else, is composed of Konggarujumeokbap, Bbongipjuk, Gamjangajji, and Odigojgammodeumbagitteok for table setting. 'General Jeong's liquor table', which allowed the general to regain his energy or was set to entertain generals of allied forces in the Myeong Dynasty, is formed by Baeksuk, Gojgamssam, Kongjukjijim and Sangsurisul. Efficacies of food materials were analyzed in the Part of Drug Formula of the best-known medical book in Asia. Foods on 'General Jeong's Table' has health efficacies that protect the five viscera and maintain the spleen and stomach.

• Food Science and Industry
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• v.54 no.2
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• pp.62-72
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• 2021

It is necessary to improve the self-management ability of Hazard Analysis Critical Control Point (HACCP) certified companies and intensive management for companies with insufficient management. In addition, the efficiency and convenience of HACCP operation should be improved by expanding and distributing smart HACCP. In this way, it is the direction that HACCP in Korea should go forward to continuously discover and expand the field of application with the improving and smartening the HACCP system.