• Title/Summary/Keyword: Drug discovery

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The Predictive QSAR Model for hERG Inhibitors Using Bayesian and Random Forest Classification Method

  • Kim, Jun-Hyoung;Chae, Chong-Hak;Kang, Shin-Myung;Lee, Joo-Yon;Lee, Gil-Nam;Hwang, Soon-Hee;Kang, Nam-Sook
    • Bulletin of the Korean Chemical Society
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    • v.32 no.4
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    • pp.1237-1240
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    • 2011

  • In this study, we have developed a ligand-based in-silico prediction model to classify chemical structures into hERG blockers using Bayesian and random forest modeling methods. These models were built based on patch clamp experimental results. The findings presented in this work indicate that Laplacian-modified naive Bayesian classification with diverse selection is useful for predicting hERG inhibitors when a large data set is not obtained.

  • https://doi.org/10.5012/bkcs.2011.32.4.1237 인용 PDF KSCI

Pharmacology of novel vanilloid receptor antagonists

  • Park, Jin-Kyu;Yi, Jung-Bum;Koh, Hyun-Ju;Jeong, Yeon-Su;Lim, Kyung-Min;Moh, Joo-Hyun;Suh, Young-Ger;Oh, Uh-Taek;Kim, Hee-Doo;Park, Hyeung-Geun;Park, Young-Ho
    • Proceedings of the PSK Conference
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    • 2002.10a
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    • pp.251.1-251.1
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    • 2002

  • Capsaicin and analogues are valuable analgesic agents when administered to mammals. including humans. However. their pungency. hypothermia and the effects on the cardiovascular and respiratory systems through their general activation of primary afferents severely limit their use. So competitive antagonists have been pursued as a novel pharmacological agent for analgesics. rather than agonists. We have identified a new class of potent and selective vanilloid receptor (VR) antagonists. (omitted)

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Drug Target Protein Prediction using SVM (SVM을 사용한 약물 표적 단백질 예측)

  • Jung, Hwie-Sung;Hyun, Bo-Ra;Jung, Suk-Hoon;Jang, Woo-Hyuk;Han, Dong-Soo
    • Proceedings of the Korean Information Science Society Conference
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    • 2007.10b
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    • pp.17-21
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    • 2007

  • Drug discovery is a long process with a low rate of successful new therapeutic discovery regardless of the advances in information technologies. Identification of candidate proteins is an essential step for the drug discovery and it usually requires considerable time and efforts in the drug discovery. The drug discovery is not a logical, but a fortuitous process. Nevertheless, considerable amount of information on drugs are accumulated in UniProt, NCBI, or DrugBank. As a result, it has become possible to try to devise new computational methods classifying drug target candidates extracting the common features of known drug target proteins. In this paper, we devise a method for drug target protein classification by using weighted feature summation and Support Vector Machine. According to our evaluation, the method is revealed to show moderate accuracy $85{\sim}90%$. This indicates that if the devised method is used appropriately, it can contribute in reducing the time and cost of the drug discovery process, particularly in identifying new drug target proteins.

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