• 미생물학회지
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• 제38권4호
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• pp.209-209
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• 2002

Mycobacterium sp. strain JCl DSM 3803 grown in methanol showed no methanol dehydrogenase or oxidase activities found in mast methylotrophic bacteria and yeasts, respectively. Even though the methanol-grown cells exhibited a little methanol-dependent oxidation by cytochrome c-dependent methanol dehydrogenase and alcohol dehydrogenase, they were not the key enzymes responsible for the methanol oxidation of the cells, in that the cells contained no c-type cytochrome and the methanol oxidizing activity from the partially purified alcohol dehydrogenase was too low, respectively. In substrate switching experiments, we found that only a catalase-peroxidase among the three types of catalase found in glucose-grown cells was highly expressed, in the methanol-grown cells and that its activity was relatively high during the exponential growth phase in Mycobacterium sp. JCl. Therefore, we propose that catalase-peroxidase is an essential enzyme responsible for the methanol metabolism directly Of indirectly in Mycobacterium sp. JCl.

• Bulletin of the Korean Chemical Society
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• 제35권11호
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• pp.3360-3362
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• 2014

• 대한약학회:학술대회논문집
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• 대한약학회 2003년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2-1
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• pp.92-92
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• 2003

The drug discovery landscape is changing rapidly in the post-genomic era. Mapping of the human genome has led to an abundance of potential drug targets. Drug discovery times and costs can be significantly reduced by developing methods for high throughput target identification/ validation, multiplexed assay development and high efficient combinatorial chemistry. (omitted)

• 한국자기공명학회논문지
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• 제19권3호
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• pp.132-136
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• 2015

Nuclear magnetic resonance (NMR) spectroscopy, owing to its ability to provide atomic level information on molecular structure, dynamics and interaction, has become one of the most powerful methods in early drug discovery where hit finding and hit-to-lead generation are mainly pursued. In recent years, drug discovery programs originating from the fragment-based drug discovery (FBDD) strategies have been widely incorporated into academia and industry in which a wide variety of NMR methods become an indispensable arsenal to elucidate the binding of small molecules onto bimolecular targets. In this review, I briefly describe FBDD and introduce NMR methods mainly used in FBDD campaigns of my company. In addition, quality control of fragment library and practical NMR methods in industrial aspect are discussed shortly.

• Journal of Microbiology and Biotechnology
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• 제31권4호
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• pp.493-500
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• 2021

Endophytic fungi are symbiotically related to plants and spend most of their life cycle within them. In nature, they have a crucial role in plant micro-ecosystem. They are harnessed for their bioactive compounds to counter human health problems and diseases. Endophytic Diaporthe sp. is a widely distributed fungal genus that has garnered much interest within the scientific community. A substantial number of secondary metabolites have been detected from Diaporthe sp. inhabited in various plants. As such, this minireview highlights the potential of Diaporthe sp. as a rich source of bioactive compounds by emphasizing on their diverse chemical entities and potent biological properties. The bioactive compounds produced are of significant importance to act as new lead compounds for drug discovery and development.

• 통합자연과학논문집
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• 제5권4호
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• pp.216-219
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• 2012

Structure-based drug design possibly benefit from in silico methods that precisely predict the binding affinity of small molecules to target macromolecules. There are many limitations arise from the difficulty of predicting the binding affinity of a small molecule to a biological target with the current scoring functions. There is thus a strong interest in novel methodologies based on MD simulations that claim predictions of greater accuracy than current scoring functions, helpful for a regular use designed for drug discovery in the pharmaceutical industry. Herein, we report a short review on free energy calculations using MMPBSA method a useful method in structure based drug discovery.

• Interdisciplinary Bio Central
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• 제2권4호
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• pp.9.1-9.5
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• 2010

Many neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are devastating disorders that affect millions of people worldwide. However, the number of therapeutic options remains severely limited with only symptomatic management therapies available. With the better understanding of the pathogenesis of neurodegenerative diseases, discovery efforts for disease-modifying drugs have increased dramatically in recent years. However, the process of translating basic science discovery into novel therapies is still lagging behind for various reasons. The task of finding new effective drugs targeting central nervous system (CNS) has unique challenges due to blood-brain barrier (BBB). Furthermore, the relatively slow progress of neurodegenerative disorders create another level of difficulty, as clinical trials must be carried out for an extended period of time. This review is intended to provide molecular and cell biologists with working knowledge and resources on CNS drug discovery and development.

• 한국IT서비스학회지
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• 제20권3호
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• pp.41-56
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• 2021

New drug discovery and development research enable clinical treatment that saves human life and improves the quality of life, but the possibility of success with new drugs is significantly low despite a long time of 14 to 16 years and a large investment of 2 to 3 trillion won in traditional methods. As artificial intelligence is expected to radically change the new drug development paradigm, artificial intelligence new drug discovery and development projects are underway in various forms of collaboration, such as joint research between global pharmaceutical companies and IT companies, and government-private consortiums. This study uses the TOE framework and the Value-based Adoption Model, and the technical, organizational, and environmental factors that should be considered for the acceptance of AI technology at the level of the new drug research organization are the value of artificial intelligence technology. By analyzing the explanatory power of the relationship between perception and intention to use, it is intended to derive practical implications. Therefore, in this work, we present a research model in which technical, organizational, and environmental factors affecting the introduction of artificial intelligence technologies are mediated by strategic value recognition that takes into account all factors of benefit and sacrifice. Empirical analysis shows that usefulness, technicality, and innovativeness have significantly affected the perceived value of AI drug development systems, and that social influence and technology support infrastructure have significant impact on AI Drug Discovery and Development systems.

• Radiation Oncology Journal
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• 제36권4호
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• pp.265-275
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• 2018

Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

• Asian-Australasian Journal of Animal Sciences
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• 제25권2호
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• pp.286-290
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• 2012

The majority of laboratory animals were transported from commercial breeders to a research facility by ground transportation. During the transportation, many biological functions and systems can be affected by stress. In this experiment, the change of body weight during the transportation was measured and the recovery periods from the transportation stress established based on the body weight changes. Total 676 laboratory animals which were aged between 3 to 9 wk old were studied. The transportation time taken from container packing to unpacking the container was approximately 24 h. The temperature of animal container was constantly maintained by air-conditioning and heating equipment. Rats were found to be more sensitive than mice. The body weight of rats was significantly decreased 3.71% (p<0.05) compared to the body weight of mice which decreased 0.9% There was no significant difference between the strains in the same species. When the changes of body weights were compared between delivery days, C57BL/6 mice showed the most variable changes compared to other species and strains. Consequently, C57BL/6 was more sensitive to stress than the other strains and the transportation process needs to be standardized to reduce between day variability. To establish the recovery periods from transportation stress, the body weight changes were measured during the acclimation period. Although the body weight of animals decreased during transportation, animals recovered their weight loss after the next day.