Hudelson Matthew G, Ketkar Nikhil S, Holder Lawrence B, Carlson Timothy J, Peng Chi-Chi, Waldher Benjamin J, Jones Jeffrey P
Department of Mathematics, Washington State University, Pullman, WA 99164-3113, USA.
J Med Chem. 2008 Feb 14;51(3):648-54. doi: 10.1021/jm701130z. Epub 2008 Jan 19.
Four different models are used to predict whether a compound will bind to 2C9 with a K(i) value of less than 10 microM. A training set of 276 compounds and a diverse validation set of 50 compounds were used to build and assess each model. The modeling methods are chosen to exploit the differences in how training sets are used to develop the predictive models. Two of the four methods develop partitioning trees based on global descriptions of structure using nine descriptors. A third method uses the same descriptors to develop local descriptions that relate activity to structures with similar descriptor characteristics. The fourth method uses a graph-theoretic approach to predict activity based on molecular structure. When all of these methods agree, the predictive accuracy is 94%. An external validation set of 11 compounds gives a predictive accuracy of 91% when all methods agree.
使用四种不同的模型来预测一种化合物是否会以小于10微摩尔的抑制常数（Ki）与2C9结合。一组由276种化合物组成的训练集和一组由50种化合物组成的多样化验证集被用于构建和评估每个模型。选择建模方法是为了利用训练集用于开发预测模型的方式上的差异。四种方法中的两种基于使用九个描述符的结构全局描述来构建划分树。第三种方法使用相同的描述符来开发将活性与具有相似描述符特征的结构相关联的局部描述。第四种方法使用基于分子结构的图论方法来预测活性。当所有这些方法都一致时，预测准确率为94%。当所有方法都一致时，一个由11种化合物组成的外部验证集的预测准确率为91%。
