In silico prediction of rhabdomyolysis of compounds by self-organizing map and support vector machine.

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.