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, PR China.
Toxicol In Vitro. 2011 Dec;25(8):2017-24. doi: 10.1016/j.tiv.2011.08.002. Epub 2011 Aug 12.
Rhabdomyolysis is a potentially lethal syndrome resulting in leakage of myocyte intracellular contents into the plasma. Some drugs, such as lipid-lowering drugs and antihistamines, can cause rhabdomyolysis. In this work, a dataset containing 186 chemical compounds causing rhabdomyolysis and 117 drugs not causing rhabdomyolysis was collected. The dataset was split into a training set (containing 230 compounds) and a test set (containing 73 compounds). A Kohonen's self-organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate compounds causing and not causing rhabdomyolysis. Using the SOM method, classification accuracies of 93.3% for the training set and 84.5% for the test set were achieved; using the SVM method, classification accuracies of 95.2% for the training set and 84.9% for the test set were achieved. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and analyzed to find the important features of molecules relating to rhabdomyolysis.
横纹肌溶解症是一种潜在致命的综合征，导致肌细胞内物质渗漏到血浆中。一些药物，如降脂药和抗组胺药，会引起横纹肌溶解症。在这项工作中，收集了一个包含 186 种导致横纹肌溶解症的化合物和 117 种不导致横纹肌溶解症的药物的数据集。该数据集被分为训练集（包含 230 种化合物）和测试集（包含 73 种化合物）。应用了 Kohonen 的自组织映射（SOM）和支持向量机（SVM）来开发分类模型，以区分导致和不导致横纹肌溶解症的化合物。使用 SOM 方法，训练集的分类准确率为 93.3%，测试集的分类准确率为 84.5%；使用 SVM 方法，训练集的分类准确率为 95.2%，测试集的分类准确率为 84.9%。此外，计算并分析了所有分子的扩展连接指纹（ECFP_4），以找到与横纹肌溶解症相关的分子的重要特征。
