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In Silico Models to Discriminate Compounds Inducing and Noninducing Toxic Myopathy

Authors

  • Xiaoying Hu,

    1. State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, P. O. Box 53, 15 BeiSanHuan East Road, Beijing 100029, P. R. China tel. +86-10-64421335; tax: +86-10-64416428
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  • Aixia Yan

    Corresponding author
    1. State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, P. O. Box 53, 15 BeiSanHuan East Road, Beijing 100029, P. R. China tel. +86-10-64421335; tax: +86-10-64416428
    • State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, P. O. Box 53, 15 BeiSanHuan East Road, Beijing 100029, P. R. China tel. +86-10-64421335; tax: +86-10-64416428
    Search for more papers by this author

Abstract

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.

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