臺灣博碩士論文加值系統

在本篇實驗中使用3D凸狀殼與傳統描述器來計算從文獻中選出的345個有活性的HIV-1蛋白脢抑制劑分子與437個從MDL/ISIS資料庫中搜尋出的非活性類似分子，將所得到的分子描述值以隨機的方式任取4∼8個描述值來代表該分子的特性，經過主成分分析(principal component analysis)的處理後再經fuzzy c-means演算法可分開成兩個群集；每一個無關聯描述的描述器經過歸屬函數(membership functions)計算後的分類結果產生一個明顯的斷開(switch)在群集的邊界上，而沒有明顯區分的分子則被歸類在群集外，並藉此統計估算其分類的準確性。在相同的3D凸狀殼描述器值下對有活性分子系列的分類上，用fuzzy c-means演算法的平均準確性上要比以線性判別函數(linear discriminant function)的直接分類法高出約20％左右；若以傳統描述器值進行相同的分類程序，則該值會提高到42％左右。接下來對於在先前分類的結果中有289個活性分子與63個非活性分子被歸類為同一群集，將其3D凸狀殼描述器值作進一步分析比較其分佈狀況後，再經過最終的分類結果仍有19個非活性分子因其在結構與拓僕特性上與一些強活性分子非常相似而無法被正確的區分出來。

We use 3D convex hull and conventional descriptors computed for 345 active HIV-1 protease inhibitors collected from literature and 437 inactive analogues searched from the MDL/ISIS database in the study. The number of descriptors used to represent each compound was from 4 to 8 randomly. These uncorrelated descriptors were divided to two groups by the principal component analysis and fuzzy c-means algorithm. The classification produced a clear-cut switch in membership functions computed for each uncorrelated descriptor at the group boundary. Compounds with non-switching membership functions computed were treated as outliers and they were counted for estimating the accuracy of the classification. The averaged accuracy of classification for the active inhibitor set was about 20 % which was better than that directly classified by a linear discriminant function on the original 3D convex hull descriptors. The whole classification scheme was also applied to several sets of some conventional descriptors computed for each compound but the averaged accuracy was around 42 % . Further classification using some 3D convex hull descriptors searched from comparing the distribution of these descriptors was performed on a new dataset composed of 289 outliers- deducted active inhibitors and 63 outliers identified from the inactive analogues through previous classification. This final classification identified 19 inactive analogues which were similar in structural and topological features to those of some highly active inhibitors classified together with them.

中文摘要 ．．．．．．．．．．．．．．．．．．．．．．．Ⅰ
英文摘要 ．．．．．．．．．．．．．．．．．．．．．．．Ⅱ
誌謝辭 ．．．．．．．．．．．．．．．．．．．．．．．．Ⅲ
前言 ．．．．．．．．．．．．．．．．．．．．．．．．．1
材料與方法 ．．．．．．．．．．．．．．．．．．．．．．8
結果與討論 ．．．．．．．．．．．．．．．．．．．．．．13
結論 ．．．．．．．．．．．．．．．．．．．．．．．．．19
參考文獻 ．．．．．．．．．．．．．．．．．．．．．．．20
圖表 ．．．．．．．．．．．．．．．．．．．．．．．．．24
附錄 ．．．．．．．．．．．．．．．．．．．．．．．．．55

1. Tabachnick, B. G.; Fidell, L. S. Using Multivariate Statistic; Harper and Row; Philadelphia, 1983.
2. Dunn, W. J., III; Wold, S. A structure-carcinogenicity study of 4-nitroquinoline 1-oxides using the SIMCA method of pattern recognition. J. Med. Chem. 1978, 21, 1001-1007.
3. Bath, P. A.; Poirrette, A. R.; Willett, P.; Allen, F. H. Similarity searching in fields of three-dimensional chemical structures: comparison of fragment-based messages of shape similarity. J. Chem. Inf. Comput. Sci. 1994, 34, 141-147.
4. Viswanadhan, V. N.; Mueller, G. A.; Basak, S. C.; Weinstein, J. N. Comparison of a neural net-based QSAR algorithm (PCANN) with hologram- and multiple linear regression-based QSAR approaches: application to 1,4-dihydropyridine-based calcium channel antagonists. J. Chem. Inf. Comput. Sci. 2001, 41, 505-511.
5. Murcia-Soler, M.; Perez-Gimenez, F.;Nalda-Molina, R.; Salabert- Salvador, M. T.;Garcia-March, F. J.; Cercos-del-Pozo, R.A.; Garrigues, T. M. QSAR analysis of hypoglycemic agents using the topological indices. J. Chem. Inf. Comput. Sci. 2001, 41, 1345-1354.
6. Godden, J. W.; Bajorath, J. Differential Shannon entropy as a sensitive measure of differences in database variability of molecular descriptors. J. Chem. Inf. Comput. Sci. 2001, 41, 1060-1066.
7. Topliss, J. G.; Edwards, R. P.; Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem. 1979, 22, 1238-1244.
8. Randic, M., Basak, S. C. A new descriptor for structure-property and structure-activity correlations. J. Chem. Inf. Comput. Sci. 2001, 41, 650-656.
9. Randic, M.; Zupan, J. On interpretation of well-known topological indices. J. Chem. Inf. Comput. Sci. 2001, 41, 550-560.
10. Dury, L.; Latour, T.; Leherte, L.; Barberis, F.; Vercauteren, D. P. A new graph descriptor for moleculars containing cycles. Application as screening criterion for searching molecular structures within large databases of organic compounds. J. Chem. Inf. Comput. Sci. 2001, 41, 1437-1445.
11. Estrada, E.; Perdomo-Lopez, I.; Torres-Labandeira, J.; Combination of 2D-, 3D-connectivity and quantum chemical descriptors in QSPR. Complexation of α- and β-cyclodextrin with benzene derivatives. J. Chem. Inf. Comput. Sci. 2001, 41, 1561-1568.
12. Estrada, E.; Molina, E.; Perdomo-Lopez, I. Can 3D structure parameters be predicted from 2D (topological) molecular descriptors. J. Chem. Inf. Comput. Sci. 2001, 41, 1015-1021.
13. Famini, G. R.; Wilson, L. Y. Using theoretical descriptors in linear salvation energy relationships. Theor. Comput. Chem. 1994, 1, 213- 241.
14. Suzuki, T.; Ide, K.;Ishida, M.;Shapiro, S. Classification of environmental extrogens by physicochemical properties using principal component analysis and hierarchical cluster analysis. J. Chem. Inf. Comput. Sci. 2001, 41, 718-726.
15. Baker, J. An algorithm for the location of transition states. J. Comput. Chem. 1986, 7, 385-395.
16. Karelson, M. Molecular Descriptors in QSAR/QSPR; Wiley- Interscience; New York, 2000.
17. Hulte´n, J.; Bonham, N. M.; Nillroth, U.; Hansson, T.; Zuccarello, G.; Bouzide, A.; Åqvist, J.; Classon, B.; Danielson, U. H.; Karle´n, A.; Kvarnstro¨m, I.; Samuelsson, B.; Hallberg, A.Cyclic HIV-1 Protease Inhibitors Derived from Mannitol: Synthesis, Inhibitory Potencies, and Computational Predictions of Binding Affinities. J. Med. Chem. 1997, 40, 885-897.
18. Alterman, M.; Bjo¨rsne, M.; Mu¨ hlman, A.; Classon, B.; Kvarnstro¨m, I.; Danielson, H.; Markgren, P. O.; Nillroth, U.; Unge, T.; Hallberg, A.; Samuelsson, B. Design and Synthesis of New Potent C2-Symmetric HIV-1 Protease Inhibitors. Use of L-Mannaric Acidas a Peptidomimetic Scaffold. J. Med. Chem. 1998, 41, 3782-3792.
19. Nugiel, D. A.; Jacobs, K.; Cornelius, L.; Chang, C.; Jadhav, P. K.; Holler, E. R.; Klabe, R. M.; Bacheler, L. T.;Cordova, B.; Garber, S.; Reid, C.; Logue, K. A.; Gorey-Feret, L.J.; Lam, G. N.; Erickson-Viitanen, S.; Seitz, S. P. Improved P1/P1’Substituents for Cyclic Urea Based HIV-1 Protease Inhibitors: Synthesis, Structure-Activity Relationship, and X-ray Crystal Structure Analysis. J. Med. Chem. 1997, 40, 1465-1474.
20. Kroemer, R. T.; Ettmayer, P.; Hecht, P. 3D-Quantitative Structure-Activity Relationship of Human Immunodeficiency Virus Type-1 Proteinase Inhibitors: Comparative Molecular Field Analysis of 2-Heterosubstituted Statine Derivatives-Implications for the Design of Novel Inhibitors. J. Med. Chem. 1995, 38, 4917-4928.
21. Scholz, D.; Billich, A.; Charpiot, B.; Ettmayer, P.; Lehr, P.; Rosenwirth, B.; Schreiner, E.; Gstach, H. Inhibitors of HIV-1 Proteinase Containing 2-Heterosubstituted 4-Amino-3-hydroxy-5- phenylpentanoic Axid: Synthesis, Enzyme Inhibition, and Antiviral Activity. J. Med. Chem. 1994, 37, 3079-3089.
22. Alterman, M.; Andersson, H. O.; Garg, N.; Ahlse´n, G.; Lo¨vgren, L.; Classon, C.; Danielson, U. H.; Kvarnstro¨m, I.; Vrang, L.; Unge, T.; Samuelsson, B.; Hallberg, A. Design and Fast Synthesis of C-Terminal Duplicated Potent C2-Symmetric P1/P1-Modified HIV-1 Protease Inhibitors. J. Med. Chem. 1999, 42, 3835-3884.
23. Debnath, A. K. Three-Dimensional Quantitative Structure-Activity Relationship Study on Cyclic Urea Derivatives as HIV-1 Protease Inhibitors: Application of Comparative Molecular Field Analysis. J. Med Chem. 1999, 42, 249-259.
24. Prabhakar K.; Jadhav, P. A.; Woerner, F. J.; Chang, C. H.; Garber, S. S.; Anton, E. D.; Bacheler, L. T. Cyclic Urea Amides: HIV-1 Protease Inhibitors with Low Nanomolar Potency against both Wild Type and Protease Inhibitor Resistant Mutants of HIV. J. Med Chem. 1997, 40, 181-191.
25. The MDL/ISIS database is installed at the National Center for High Performance Computing, Taiwan, ROC. The URL is http://saturn.nchc. gov.tw:9091/cds.
26. Lin, T. H.; Yu, Y. S.; Chen, H. J. Classification of some active compounds and their inactive analogues using two three-dimensional molecular descriptors derived from computation of three-dimensional convex hulls for structures theoretically generated for them. J. Chem. Inf. Comput. Sci. 2000, 40, 1210-1221.
27. Selim, S. Z.; Kamel, M. S. On the mathematical and numerical properties of the fuzzy c-means algorithm. Fuzzy Sets and Systems 1992, 49, 181-191.
28. Yen, J.; Langari, R. Fuzzy Logic Intelligence, Control, and Information; Prentice Hall; New Jersey, 1999.
29. James, M. Classification Algorithms; Collins; London, 1985.
30. Lam, P. Y.; Ru, Y.; Jadhav, P. K.; Alrich, P. E.; DeLucca, G. V.; Eyermann, C. J.; Chang, C. H.; Emmett, G.; Holler, E. R.; Daneker, W. F.; Li, L.; Confalone, P. N.; McHugh, R. J.; Han, Q.; Li, R.; Markwalder, J. A.; Seitz, S. P.; Sharpe, T. R.; Bacheler, L. T.; Rayner, M. M.; Klabe, R. M.; Shum, L.; Winslow, D. L.; Kornhauser, D. M.; Hodge, C. N. Cyclic HIV protease inhibitors: synthesis, conformational analysis, P2/P2’ structure-activity relationship and molecular recognition of cyclic ureas. J. Med. Chem. 1996, 39, 3514- 3525.
31. Kuntz, I. D.; Meng, E.C.; Shoichet, B. K. Structure-based molecular design. Acc. Chem. Res. 1994, 27, 117-123.
32. SYBYL 6.7; The Tripos Associates, 1699 S. Hanley Rd., St. Louis, MO.
33. Miller, M.; Schneider, J.; Sathyanarayana, B. K.; Toth, M. V.; Marshall, G. R.; Clawson, L.; Selk, S.; Kent, S. B. H.; Wlodawer, A. Structure of the complex of synthetic HIV-1 protease with a substrate-based inhibitor at 2.3 angstroms resolution. Science 1989, 246, 1149-1152.
34. Xu, J.; Stevenson, J. Drug-like index: a new approach to measure drug-like compounds and their diversity. J. Chem. Inf. Comput. Sci. 2000, 40, 1177-1187.
35. Xie, X. L.; Beni, G. A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Machine Intell. 1991, PAMI-13,841-847.
36. Fisher, R. A. The use of multiple measurements in taxonomic problem. Ann. Eugenics 1936, 7, 179-188.
37. Maxwell, A. E. Multivariate Analysis in Behavioural Research; Chapman and Hall, New York, 1977.
38. Gustafson, D. E.; Kessel, W. C. Fuzzy clustering with a fuzzy covariance matrix. Proc. IEEE CDC, 1979, 761-766.
39. Krishnapuram, R.; Keller, J. M. A possibilistic approach to clustering. IEEE Trans. On Fuzzy Systems 1993, 1, 98-110.