Evaluation of machine-learning methods for ligand-based virtual screening
Evaluation of machine-learning methods for ligand-based virtual screening
复制标题
DOI：
10.1007/s10822-006-9096-5
复制
发表时间：
2007-01-01
影响因子：
3.5
通讯作者：
Stiefl, Nikolaus
中科院分区：
文献类型：
--
作者：
Chen, Beining;Harrison, Robert F.;Stiefl, Nikolaus
机器学习方法可以通过分析已知（in）活性分子的结构特征来进行虚拟筛选，我们在这里讨论核判别和朴素贝叶斯分类器（NBC）方法的使用。我们报告了一种核方法，该方法允许处理由二进制、整数和实值描述符表示的分子，并表明它在筛选性能上与先前描述的专门用于分析分子结构的二进制指纹表示的核方法没有什么不同。然后，当训练集只包含很少的活性分子时，我们评估NBC的性能。在这种情况下，基于组融合的更简单的方法似乎可以提供更好的筛选性能，特别是当要处理结构异构的数据集时。
Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.