Premium
Support Vector Machine (SVM) Models for Predicting Inhibitors of the 3′ Processing Step of HIV‐1 Integrase
Author(s) -
Xuan Shouyi,
Wang Maolin,
Kang Hang,
Kirchmair Johannes,
Tan Lu,
Yan Aixia
Publication year - 2013
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201300107
Subject(s) - support vector machine , integrase , integrase inhibitor , human immunodeficiency virus (hiv) , computer science , computational biology , vector (molecular biology) , machine learning , artificial intelligence , data mining , chemistry , virology , biology , antiretroviral therapy , biochemistry , viral load , gene , recombinant dna
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 IC 50 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.