In Silico Models to Discriminate Compounds Inducing and Noninducing Toxic Myopathy

Toxic myopathy is a muscular disease in which the muscle fibers do not function and which results in muscular weakness. Some drugs, such as lipid-lowering drugs and antihistamines, can cause toxic myopathy. In this work, a dataset containing 232 chemical compounds inducing toxic myopathy (IM-compounds) and 117 drugs not inducing toxic myopathy (notIM-compounds) was collected. The dataset was split into a training set (containing 270 compounds) and a test set (containing 79 compounds). A Kohonen's self-organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate IM-compounds and notIM-compounds. Polarizibity related descriptors, electronegativity related descriptors, atom charges related descriptors, H-bonding related descriptor, atom identity and molecular shape descriptors were used to build models. Using the SOM method, classification accuracies of 88.4 % for the training set and 88.2 % for the test set were achieved; using the SVM method, classification accuracies of 95.6 % for the training set and 86.1 % for the test set were achieved. In addition, extended connectivity fingerprints (ECFP_4) were calculated and analyzed to find important substructures of molecules relating to toxic myopathy.