Developing Trend of nSAR Modeling and Pesticides

정량적 구조 활성관계(QSAR) 모델개발 동향과 농약

  • Ock, Hwan-Suck (Reseat Program, Korea Institute of Science and Technology Information)
  • 옥환석 (한국과학기술정보연구원 Resent 프로그램)
  • Received : 2011.03.10
  • Accepted : 2011.03.22
  • Published : 2011.03.31

Abstract

Keywords

References

  1. 성낙도 (2002), 정량적인 구조-활성상관(QSAR) 기법에 의한 새로운 농약의 개발 I. 기본 개념과 QSAR 기법의 유형, 농약과학회지, 6(3), 166-174.
  2. 성낙도 (2002), 정량적인 구조-활성상관(QSAR) 기법에 의한 새로운 농약의 개발 II. 자유에너지 직선관계(LFER)와 설명인자들, 농약과학회지 6(4), 231-243.
  3. 성낙도 (2003) 정량적인 구조-활성상관(QSAR) 기법에 의한 새로운 농약의 개발.III. 3D QSAR 기법들과 컴퓨터를 이용한 분자설계(CAMD), 농약과학회지 7(1), 1-11.
  4. 성낙도 (2003) 정량적인 구조-활성상관(QSAR) 기법에 의한 새로운 농약의 개발 - IV. 국내의 연구 동향과 전망, Journal of the Korean Society for Applied Biological Chemistry, 46(3), pp. 155-164.
  5. 성낙도, 송선섭 (2003a), 제초제의 활성성분에 대한 물리화학 파라미터의 범위, 학국농약과학회지 7(1), 58-65.
  6. 성낙도, 송선섭 (2003b) 작물보호제로서 살군제와 살충제의 활성성분에 대한 물리-화학 파라미터의 범위, J. Korean Soc. for Agric. Chem. Biotechnol. 46(4), 280-284.
  7. Akinori Hirashim, Eiichi Kuwano, Morifusa Eto (2003), Com-parative receptor surface analysis of octopaminergic antagonists for the locust neuronal octopamine receptor, Computational Biology and Chemistry 27:531-540. https://doi.org/10.1016/j.compbiolchem.2003.07.001
  8. Andrade, C.; Salum, L.B.; Castilho, M.; Pasqualoto, K.; Ferreira, E.; Andricopulo, A. (2008), Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents. Mol. Divers. 12, 47-59. https://doi.org/10.1007/s11030-008-9074-z
  9. Casalegno M.; Sello G. Benfenati E. (2006), Top-Priority Frag-ment QSAR Approach in Predicting Pesticide Aquatic Toxicity. Chem. Res. Toxicol. 19, 1533-1539. https://doi.org/10.1021/tx0601814
  10. Chunyan Zhao, Elena Boriani, Antonio Chana, Alessandra Roncaglioni, Emilio Benfenati (2008), A new hybrid system of QSAR models for predicting bioconcentration factors (BCF), Chemosphere 73:1701-1707. https://doi.org/10.1016/j.chemosphere.2008.09.033
  11. Cramer, R.; Patterson, D.; Bunce, J. 1988, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 5959-5967. https://doi.org/10.1021/ja00226a005
  12. D. J. de Ridder, L. Villacorte, A.R.D. Verliefde, J.Q.J.C. Verberk, S.G.J. Heijman, G.L. Amy, J.C. van Dijk (2010), Modeling equilibrium adsorption of organic micropollutants onto activated carbon, water research 44:3077-3086 https://doi.org/10.1016/j.watres.2010.02.034
  13. Frank E. Blaney, Dorica Naylor, and John Woods (1993), MAMBAs: A real-time graphics environment for QSAR, J. Mol. Graphics 11: September 157-165. https://doi.org/10.1016/0263-7855(93)80067-2
  14. Hopfinger, A.J.; Wang, S.; Tokarski, J.S.; Jin, B.; Albuquerque, M.; Madhav, P.J.; Duraiswami, C. (1997), Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J. Am. Chem. Soc. 119, 10509-10524. https://doi.org/10.1021/ja9718937
  15. Joop Hermens (1995), Prediction of environmental toxicity based on structure-activity relationships using mechanistic information, The Science of the Total Environment 171: 235-242. https://doi.org/10.1016/0048-9697(95)04684-5
  16. Klebe, G.; Abraham, U., Mietzner, T. (1994), Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 37, 4130-4146. https://doi.org/10.1021/jm00050a010
  17. Kyaw Zeyar Myint, Xiang-Qun Xie, (2010) Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods, Int. J. Mol. Sci., 11: 3846-3866. https://doi.org/10.3390/ijms11103846
  18. Linnan He, Peter C. Jurs (2005), Assessing the reliability of a QSAR model's predictions, Journal of Molecular Graphics and Modelling 23:503-523. https://doi.org/10.1016/j.jmgm.2005.03.003
  19. Luis G. Valerio Jr. (2009), In silico toxicology for the phar-maceutical sciences, Toxicology and Applied Pharmacology 241:356-370. https://doi.org/10.1016/j.taap.2009.08.022
  20. Mark T.D. Cronin, T. Wayne Schultz (2003), Pitfalls in QSAR, Journal of Molecular Structure (Theochem) 622:39-51. https://doi.org/10.1016/S0166-1280(02)00616-4
  21. Markus A. Lill (2007), Multi-dimensional QSAR in drug discovery, Drug Discovery Today 12(23/24), pp. 1013-1017. https://doi.org/10.1016/j.drudis.2007.08.004
  22. Markus Boehm (2011), Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries, pp. 5.
  23. Matthew S. Sigman, Jeremie J. Miller (2009), Examination of the Role of Taft-Type Steric Parameters in Asymmetric Catalysis, J. Org. Chem. 74:7633-7643. https://doi.org/10.1021/jo901698t
  24. Nicholas E. Jewell, David B. Turner, Peter Willett, Graham J. Sexton (2001), Automatic generation of alignments for 3D QSAR analyses, Journal of Molecular Graphics and Modelling 20:111-121. https://doi.org/10.1016/S1093-3263(01)00110-3
  25. Ourania Peristera, Morena Spreafico, Martin Smiesko, Beat Ernst, Angelo Vedani (2009), Mixed-model QSAR at the human mineralocorticoid receptor: Predicting binding mode and affinity of anabolic steroids, Toxicology Letters 189:219-24. https://doi.org/10.1016/j.toxlet.2009.05.025
  26. Ourania Peristera, Morena Spreafico, Martin Smiesko, Beat Ernst, Angelo Vedani. (2009) Mixed-model QSAR at the human mineralocorticoid receptor: Predicting binding mode and affinity of anabolic steroids, Toxicology Letters 189, 219- 224. https://doi.org/10.1016/j.toxlet.2009.05.025
  27. Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. (2000), GRid-INdependent descriptors (GRIND): A novel class of alignment-independent three-dimensional molecular descriptors. J. Med. Chem. 43, 3233-3243. https://doi.org/10.1021/jm000941m
  28. Robinson, D.D.; Winn, P.J.; Lyne, P.D.; Richards, W.G. (1999), Self-organizing molecular field analysis: A tool for structure-activity studies. J. Med. Chem. 42, 573-583. https://doi.org/10.1021/jm9810607
  29. Shane Weaver, M. Paul Gleeson (2008), The importance of the domain of applicability in QSAR modeling, Journal of Molecular Graphics and Modelling 26:1315-1326. https://doi.org/10.1016/j.jmgm.2008.01.002
  30. Shunlai Li, Yan Zheng (2006), Self-Organizing Molecular Field Analysis on a New Series of COX-2 Selective Inhibitors: 1,5-Diarylimidazoles, Int. J. Mol. Sci. 7, 220-229. https://doi.org/10.3390/i7070220
  31. Silverman, B.D.; Platt, D.E. (1996), Comparative molecular moment analysis (CoMMA): 3D-QSAR without molecular superposition. J. Med. Chem. 39, 2129-2140. https://doi.org/10.1021/jm950589q
  32. Supratik Kar, Kunal Roy (2010), QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors, Journal of Hazardous Materials 177 : 344-351 https://doi.org/10.1016/j.jhazmat.2009.12.038
  33. Vivek VYAS, Anurekha JAIN, Avijeet JAIN, Arun GUPTA (2008), Virtual Screening: A Fast Tool for Drug Design, Sci Pharm. 76:333-360. https://doi.org/10.3797/scipharm.0803-03
  34. Wagener, M.; Sadowski, J.; Gasteiger, J. (1995), Autocorrelation of molecular surface properties for modeling corticosteroid binding globulin and cytosolic ah receptor activity by neural networks. J. Am. Chem. Soc. 117, 7769-7775. https://doi.org/10.1021/ja00134a023
  35. Yovani Marrero Ponce (2004), Total and local (atom and atom type) molecular quadratic indices: significance interpretation, comparison to other molecular descriptors, and QSPR/QSAR applications Bioorganic & Medicinal Chemistry 12:6351-6369. https://doi.org/10.1016/j.bmc.2004.09.034
  36. Zhokhova, N.; Baskin, I.; Palyulin, V.; Zefirov, A.; Zefirov, N. (2007), Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies. Doklady Chem. 417, 282-284. https://doi.org/10.1134/S0012500807120026