Enzyme Classification on DUD-E Database Using Logistic Regression Ensemble (Lorens)
Enzyme Classification on DUD-E Database Using Logistic Regression Ensemble (Lorens)
复制标题
使用 Logistic 回归集成 (Lorens) 对 DUD-E 数据库进行酶分类
DOI：
10.1007/978-3-319-66984-7_6
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ohwada H
中科院分区：
文献类型：
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作者：
Kuswanto H.;Melasasi J.N.;Ohwada H
药物的发现一直是一个复杂的过程，耗时且昂贵，直到发现了制备药物的替代方案，即使用计算机模拟方法来发现潜在的抑制剂。在药物设计过程中，通过对接评分步骤进行化合物分类。本研究的目的是使用适当的分类方法，即基于计算的方法和标准统计方法来预测对接评分结果。本研究检测了DUD-E数据库中的三种靶酶aofb、cah 2和hs 90 a。每种酶由不同的化合物组成，这些化合物被分为好的抑制剂（配体）和坏的抑制剂（诱饵）。在这项研究中，对接评分步骤进行二元逻辑回归和逻辑回归集成（Lorens）。二元逻辑回归分析得出aofb的准确率为90.4%，cah 2的准确率为91.7%，hs 90 a的准确率为94%。同时，逻辑回归集成（Lorens）的准确率分别为88.95%，92.1%和100%的aofb，cah 2和hs 90 a。本文表明，逻辑回归集成方法优于标准逻辑回归用于抑制剂分类。
Discovery of drugs has been a complex process, time-consuming and expensive until an alternative of making drug has been found i.e. using in silico method to discover potential inhibitor. During the process of drug design, compound classification is carried out through docking score steps. The aim of this research is to predict the docking score results using proper methods for classification i.e. a computationally based method and a standard statistical method. This research examined three target enzymes listed in DUD-E database i.e. aofb, cah2 and hs90a. Each enzyme consists of different compounds that will be classified as good inhibitor (ligand) and bad inhibitor (decoy). In this research, the docking score step is conducted by binary logistic regression and logistic regression ensemble (Lorens). Binary logistic regression yields on 90.4% of accuracy for aofb, 91.7% for cah2 and 94% for hs90a enzyme. Meanwhile, logistic regression ensemble (Lorens) results on the accuracy levels of 88.95, 92.1 and 100% for aofb, cah2 and hs90a consecutively. This paper showed that logistic regression ensemble method outperforms standard logistic regression to be used for the inhibitor classification.