• Proceedings of the PSK Conference
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• 2002.10a
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• pp.86-87
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• 2002

Recent technological innovations in the drug discovery process such as combinatorial synthesis and high throughput screening have led to the identification of an increasingly large number of compounds at the hits-to-leads stage. Therefore, rapid and precise pharmacokinetic/metabolic screening is essential to enhance the tractability of selected leads and to minimize the risk of failure in the later stages of drug development. (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.

• Biomolecules & Therapeutics
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• v.21 no.2
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• pp.89-96
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• 2013

Atherosclerotic vascular dysfunction is a chronic inflammatory process that spreads from the fatty streak and foam cells through lesion progression. Therefore, its early diagnosis and prevention is unfeasible. Reactive oxygen species (ROS) play important roles in the pathogenesis of atherosclerotic vascular disease. Intracellular redox status is tightly regulated by oxidant and antioxidant systems. Imbalance in these systems causes oxidative or reductive stress which triggers cellular damage or aberrant signaling, and leads to dysregulation. Paradoxically, large clinical trials have shown that non-specific ROS scavenging by antioxidant vitamins is ineffective or sometimes harmful. ROS production can be locally regulated by cellular antioxidant enzymes, such as superoxide dismutases, catalase, glutathione peroxidases and peroxiredoxins. Therapeutic approach targeting these antioxidant enzymes might prove beneficial for prevention of ROS-related atherosclerotic vascular disease. Conversely, the development of specific antioxidant enzyme-mimetics could contribute to the clinical effectiveness.

• YAKHAK HOEJI
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• v.59 no.4
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• pp.151-157
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• 2015

The purpose of this study is to demonstrate how lead compounds are best optimized with the application of in silico QSAR and PBPK modeling at the early drug discovery stage. Several predictive QSAR models such as $IC_{50}$ potency model, intrinsic clearance model and brain penetration model were built and applied to a set of virtually synthesized library of the BACE1 inhibitors. Selected candidate compounds were also applied to the PBPK modeling for comparison between the predicted animal pharmacokinetic parameters and the observed ones in vivo. This novel lead optimization strategy using QSAR and PBPK modelings could be helpful to expedite the drug discovery process.

• 한국생물공학회:학술대회논문집
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• 2000.11a
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• pp.119-122
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• 2000

Biological science is being revolutionized by the availability of much sequence information from many genome project With the advanced technology at hand, main trend in biological research is rapidly changing from a structural DNA analysis to understanding cellular function of the DNA sequences. Combined with mechanics, computer, bioinformatics and other advanced technologies, DNA chip technology provides numerous applications because of its robustness, accuracy, and automation. DNA chip is expected to become an indispensable tool in fields of biology, biotechnology, drug discovery, and other application areas. DNA chip can be used for mutation and polymorphism detection, gene expression monitoring and phenotypic analysis as well. If DNA chip is used for the development of pharmaceutical products, it can considerably reduce the cost and time for the entire process of drug discovery and development, and can also contribute in developing personal drugs.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.109-117
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• 2020

An edutainment system aims to help learners to recognize problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Edutainment contents can be usefully applied to education and training in the both scientific and industrial areas. Our present work proposes an edutainment system that can be applied to a drug discovery process including virtual screening by using intuitive multi-modal interfaces. In this system, a stereoscopic monitor is used to make three-dimensional (3D) macro-molecular images, with supporting multi-modal interfaces to manipulate 3D models of molecular structures effectively. In this paper, our system can easily solve a docking simulation function, which is one of important virtual drug screening methods, by applying gaming factors. The level-up concept is implemented to realize a bio-game approach, in which the gaming factor depends on number of objects and users. The quality of the proposed system is evaluated with performance comparison in terms of a finishing time of a drug docking process to screen new inhibitors against target proteins of human immunodeficiency virus (HIV) in an e-drug discovery process.

• Interdisciplinary Bio Central
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• v.4 no.1
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• pp.2.1-2.7
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• 2012

Introduction: Histone deacetylases (HDAC) are a class of enzymes that remove acetyl groups from ${\varepsilon}$-N-acetyl lysine amino acids of histone proteins. Their action is opposite to that of histone acetyltransferase that adds acetyl groups to these lysines. Only few HDAC inhibitors are approved and used as anti-cancer therapeutics. Thus, discovery of new and potential HDAC inhibitors are necessary in the effective treatment of cancer. Materials and Methods: This study proposed a method using support vector machine (SVM) to classify HDAC8 inhibitors and non-inhibitors in early-phase virtual compound filtering and screening. The 100 experimentally known HDAC8 inhibitors including 52 inhibitors and 48 non-inhibitors were used in this study. A set of molecular descriptors was calculated for all compounds in the dataset using ADRIANA. Code of Molecular Networks. Different kernel functions available from SVM Tools of free support vector machine software and training and test sets of varying size were used in model generation and validation. Results and Conclusion: The best model obtained using kernel functions has shown 75% of accuracy on test set prediction. The other models have also displayed good prediction over the test set compounds. The results of this study can be used as simple and effective filters in the drug discovery process.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• Molecular & Cellular Toxicology
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• v.3 no.4
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• pp.222-230
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• 2007

Some drugs may be limited in their clinical application due to their propensity towards their adverse effects. Toxicogenomic technology represents a useful approach for evaluating the toxic properties of new drug candidates early in the drug discovery process. Nitrofurantoin (NF) is clinical chemotherapeutic agent and antimicrobial and used to treatment of urinary tract infections. However, NF has been shown to result in pulmonary toxic effects. In this research, we revealed the changing expression gene profiles in BEAS-2B, human bronchial epithelial cell line, exposed to NF by using human oligonucleotide chip. Through the clustering analysis of gene expression profiles, we identified 136 up-regulated genes and 379 down-regulated genes changed by more than 2-fold by NF. This study identifies several interesting targets and functions in relation to NF-induced toxicity through a gene ontology analysis method including biological process, cellular components, molecular function and KEGG pathway.