Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
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DOI：
10.1002/qsar.200810173
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发表时间：
2009-07-01
期刊：
影响因子：
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通讯作者：
Winkler, D. A.
中科院分区：
文献类型：
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作者：
Burden, F. R.;Winkler, D. A.
选择一组分子描述符（功能），是最相关的一个给定的生物反应变量是一个非常重要的问题，在定量构效关系，尚未解决的最佳鲁棒的方式。这是一类有趣而重要的数学问题，其中变量的数量大大超过观测的数量（严重欠定系统）。我们已经使用了两种贝叶斯方法来进行这项任务，使用一套QSAR数据集。我们采用了一种专门的稀疏贝叶斯特征约简方法，该方法基于EM算法和拉普拉斯算子，然后选择一小部分最相关的描述符，用于从更大的可能性池中对响应变量进行建模。在有监督的方式选择了最佳的描述符，我们使用贝叶斯正则化神经网络进行非线性回归，并获得强大的简约QSAR模型的五个药物数据集。模型进行了验证，使用独立的测试集，并与其他当代描述符选择方法的结果进行比较。还详细讨论了验证小型定量构效关系数据集的问题。稀疏特征选择算法被证明是一个优秀的，强大的方法选择描述符的QSAR模型，因为它是监督（描述符选择的上下文相关的方式），简约（模型不过于复杂），和内在的解释。再加上一个强大的简约的非线性建模方法，如贝叶斯正则化神经网络，组合提供了一种最佳的数据建模的手段，并允许解释的模型在最相关的描述符。
Choosing a set of molecular descriptors (features) that is most relevant to a given biological response variable is a very important problem in QSAR that has not be solved in an optimal robust way. It is an interesting and important class of mathematical problems, where the number of variables greatly outweighs the number of observations (grossly underdetermined systems). We have used two Bayesian approaches to carry out this task using a suite of QSAR data sets. We employed a specialized sparse Bayesian feature reduction method based on an EM algorithm with a Laplacian prior to select a small set of the most relevant descriptors for modeling the response variables from a much larger pool of possibilities. Having chosen the optimum descriptors in a supervised manner, we used a Bayesian regularized neural network to carry out nonlinear regression and derive robust parsimonious QSAR models for five drug data sets. Models were validated using independent test sets, and results compared with other contemporary descriptor selection methods. Issues around validating small QSAR data sets were also discussed in detail. The sparse feature selection algorithm proved to be an excellent, robust method for selecting descriptors for QSAR models, as it is supervised (descriptors chosen in a context-dependent manner), parsimonious (models not overly complex), and inherently interpretable. Coupled to a robust parsimonious nonlinear modeling method such as the Bayesian regularized neural net, the combination provides a means of optimally modeling the data, and allowing interpretation of the model in terms of the most relevant descriptors.