Replacement Method and Enhanced Replacement Method Versus the Genetic Algorithm Approach for the Selection of Molecular Descriptors in QSPR/QSAR Theories
Replacement Method and Enhanced Replacement Method Versus the Genetic Algorithm Approach for the Selection of Molecular Descriptors in QSPR/QSAR Theories
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DOI：
10.1021/ci100103r
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发表时间：
2010-09-01
影响因子：
5.6
通讯作者：
Castro, Eduardo A.
中科院分区：
文献类型：
--
作者：
Mercader, Andrew G.;Duchowicz, Pablo R.;Castro, Eduardo A.
我们比较了三种方法，从更大的池，这样的回归变量的分子描述符的最佳子集的选择。一方面是我们的增强替换方法（ERM），另一方面是简单的替换方法（RM）和遗传算法（GA）。这些方法避免了在大量的分子描述符中不切实际地搜索最佳变量。目前的结果为10个不同的实验数据库表明，ERM显然是优选的GA是略优于RM。然而，后一种方法需要最少量的线性回归，因此计算时间最短。
We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.