Liu Ruizi, Nie Xianglei, Zhong Min, Hou Xiaoli, Xuan Shouyi, Yan Aixia
State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P.R. China.
Comb Chem High Throughput Screen. 2014 Feb;17(2):114-23. doi: 10.2174/13862073113166660063.
Using Self-Organizing Map (SOM) and Support Vector Machine (SVM), four classification models were built to predict whether a compound is an active or weakly active inhibitor of Aurora B kinase. A dataset of 679 Aurora B kinase inhibitors was collected, and randomly split into a training set (278 active and 204 weakly active inhibitors) and a test set (109 active and 88 weakly active inhibitors). Based on 19 selected ADRIANA.Code descriptors and 135 MACCS fingerprints, all the four models showed a good prediction accuracy of over 87% on the test set. It benefited from the advantages of two different types of molecular descriptors in encoding structure information of compounds and characterizing the diversity of different inhibitors. Some molecular properties, such as hydrogen-bonding interactions and atom charge related descriptors were found to be important to the bioactivity of Aurora B kinase inhibitors.
利用自组织映射（SOM）和支持向量机（SVM）构建了四个分类模型，以预测一种化合物是否为Aurora B激酶的活性或弱活性抑制剂。收集了一个包含679种Aurora B激酶抑制剂的数据集，并随机分为训练集（278种活性抑制剂和204种弱活性抑制剂）和测试集（109种活性抑制剂和88种弱活性抑制剂）。基于19个选定的ADRIANA.Code描述符和135个MACCS指纹，所有四个模型在测试集上均显示出超过87%的良好预测准确率。这得益于两种不同类型的分子描述符在编码化合物结构信息和表征不同抑制剂多样性方面的优势。发现一些分子性质，如氢键相互作用和与原子电荷相关的描述符对Aurora B激酶抑制剂的生物活性很重要。
