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研究生:王靖銘
研究生(外文):Ging Ming Wang
論文名稱:使用不相關的三維分子描述器與模糊理論分類一群有活性的HIV-1蛋白脢抑制劑分子與其無活性的相似物
論文名稱(外文):Classification of some active HIV-1 protease inhibitors and their inactive analogues using some uncorrelated three-dimensional molecular descriptors and a fuzzy c-means algorithm
指導教授:林志侯
指導教授(外文):Thy Hou Lin
學位類別:碩士
校院名稱:國立清華大學
系所名稱:生命科學系
學門:生命科學學門
學類:生物學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:68
中文關鍵詞:3D凸狀殼模糊理論
外文關鍵詞:3D convex hullfuzzy c-means
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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

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