• Genomics & Informatics
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• v.17 no.2
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• pp.18.1-18.10
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• 2019

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

• Knowledge Management Research
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• v.2 no.1
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• pp.109-132
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• 2001

The purpose of this article is to develop a dynamic model of organizational capabilities and knowledge creation, and at the same time identify the organizational capability factors for effective knowledge creation, by empirically analyzing the history of new Quinolone antibacterial drug compound (LB20304a) discovery process at LG, as a case in point. Major findings of this study are as follows. First, in a science-based area such as drug development, the core of successful knowledge creation lies in creative combination of different bodies of scientific explicit knowledge. Second, the greater the difficulty of learning external knowledge, the more tacit knowledge is needed for the recipient firm to effectively exploit that knowledge. Third, in science-based sector such as pharmaceutical industry, the key for successful knowledge creation lies in the capability of recruiting and retaining star scientists. Finally, for effective knowledge creation, a firm must keep its balance among three dimensions of organizational capabilities: local, process, architectural capabilities.

• The Plant Pathology Journal
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• v.33 no.6
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• pp.529-542
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• 2017

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

• Interdisciplinary Bio Central
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• v.2 no.4
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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.

• BMB Reports
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• v.43 no.5
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• pp.305-310
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• 2010

Syndecans, cell surface heparansulfate proteoglycans, have been proposed to act as cell surface receptors and/or coreceptors to play critical roles in multiple cellular functions. However, recent reports suggest that the function of syndecans can be further extended through shedding, a cleavage of extracellular domain. Shedding constitutes an additional level for controlling the function of syndecans, providing a means to attenuate and/or regulate amplitude and duration of syndecan signals by modulating the activity of syndecans as cell surface receptors. Whether these remaining cleavage products are still capable of functioning as cell surface receptors to efficiently transduce signals inside of cells is not clear. However, shedding transforms cell surface receptor syndecans into soluble forms, which, like growth factors, may act as novel ligands to induce cellular responses by association with other cell surface receptors. It is becoming interestingly evident that shed syndecans also contribute significantly to syndecan functions in cancer biology. This review presents current knowledge about syndecan shedding and its functional significance, particularly in the context of cancer.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.183-192
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• 2009

ADESS(Adverse drug event surveillance system) is the system which distinguishes adverse drug events using adverse drug signals. This system shows superior effectiveness in adverse drug surveillance than current methods such as volunteer reporting or char review. In this study, we built clinical data mart(CDM) for the development of ADESS. This CDM could obtain data reliability by applying data quality management and the most suitable clustering number(n=4) was gained through the reproducibility assessment in unsupervised learning techniques of knowledge discovery. As the result of analysis, by applying the clustering number(N=4) K-means, Kohonen, and two-step clustering models were produced and we confirmed that the K-means algorithm makes the most closest clustering to the result of adverse drug events.

• Proceedings of the Korea Database Society Conference
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• 1998.09a
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• pp.83-98
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• 1998

Who needs Media Asset Management？ ◆ Publishers ◆ Any company publishing newspapers, magazines, catalogs or web sites. ◆ Content Creators ◆ Companies who create content for use in their business ◆ Broadcasters, Advertising Agencies, Studios, Sports Houses (NBA, NFL), Corporate Training Depts, Retailers ◆ Content Distributors ◆ Cable Operators, Telecoms, Internet Service Providers, Online Service Providers Who needs Media Asset Management？ ◆ There's a LOT of money being spent on this kind of technology, and not just by 'media' companies ◆ Retailers, for catalogs, web sites, call centers ◆ Chems/Pharms, for drug. discovery, knowledge management ◆ Legal, for document and knowledge management ◆ Federal, for video surveillance and knowledge management ◆ Manufacturing, for integration of CAD, text and business-to-business applications ◆ Anyone with a Web/Content Management challenge(omitted)

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.329-342
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• 2021

The pharmaceutical industry is experiencing a productivity crisis with a low probability of success despite a long period of time and enormous cost. As a strategy to solve the productivity crisis, the use cases of Artificial Intelligence(AI) and Bigdata are increasing worldwide and tangible results are coming out. However, domestic pharmaceutical companies are taking a wait-and-see attitude to adopt AI platform for drug research. This study proposed a research model that combines the Value-based Adoption Model and the Innovation Resistance Model to empirically study the effect of value perception and resistance factors on adopting AI Platform. As a result of empirical verification, usefulness, knowledge richness, complexity, and algorithmic opacity were found to have a significant effect on perceived values. And, usefulness, knowledge richness, algorithmic opacity, trialability, technology support infrastructure were found to have a significant effect on the innovation resistance.

• Molecules and Cells
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• v.28 no.3
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• pp.139-148
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• 2009

Cell death has been traditionally classified in apoptosis and necrosis. Apoptosis, known as programmed cell death, is an active form of cell death mechanism that is tightly regulated by multiple cellular signaling pathways and requires ATP for its appropriate process. Apoptotic death plays essential roles for successful development and maintenance of normal cellular homeostasis in mammalian. In contrast to apoptosis, necrosis is classically considered as a passive cell death process that occurs rather by accident in disastrous conditions, is not required for energy and eventually induces inflammation. Regardless of different characteristics between apoptosis and necrosis, it has been well defined that both are responsible for a wide range of human diseases. Glycogen storage disease type I (GSD-I) is a kind of human genetic disorders and is caused by the deficiency of a microsomal protein, glucose-6-phosphatase-${\alpha}$ ($G6Pase-{\alpha}$) or glucose-6-phosphate transporter (G6PT) responsible for glucose homeostasis, leading to GSD-Ia or GSD-Ib, respectively. This review summarizes cell deaths in GSD-I and mostly focuses on current knowledge of the neutrophil apoptosis in GSD-Ib based upon ER stress and redox signaling.