Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV-1 Integrase

Authors

  • Shouyi Xuan,

    1. 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 tel: +86-10-64421335, fax: +86-10-64416428
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  • Maolin Wang,

    1. 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 tel: +86-10-64421335, fax: +86-10-64416428
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  • Hang Kang,

    1. 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 tel: +86-10-64421335, fax: +86-10-64416428
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  • Johannes Kirchmair,

    1. Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, UK
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  • Lu Tan,

    1. Cambridge Institute for Medical Research, Wellcome Trust/MRC Building, Addenbrooke's Hospital, University of Cambridge, Hills Road, CB2 0XY, Cambridge, UK
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  • Aixia Yan

    Corresponding author
    1. 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 tel: +86-10-64421335, fax: +86-10-64416428
    • 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 tel: +86-10-64421335, fax: +86-10-64416428

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Abstract

Inhibition of the 3′ processing step of HIV-1 integrase by small molecule inhibitors is one of the most promising strategies for the treatment of AIDS. Using a support vector machine (SVM) approach, we developed six classification models for predicting 3′P inhibitors. The models are based on up to 48 selected molecular descriptors and a comprehensive data set of 1253 molecules, with measured activities ranging from nanomolar to micromolar IC50 values. Model B2, the most robust SVM model, obtains a prediction accuracy, sensitivity, specificity and Matthews correlation coefficient (MCC) of 93 %, 81 %, 94 % and 0.67 on the test set, respectively. The presence of hydrogen bonding features and hydrophilicity in general were identified as key determinants of inhibitory activity. Further important properties include molecular refractivity, π atom charge, total charge, lone pair electronegativity, and effective atom polarizability. Comparative fragment-based analysis of the active and inactive molecules corroborated these observations and revealed several characteristic structural elements of 3′P inhibitors. The models built in this study can be obtained from the authors.

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