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Study of Structure‐active Relationship for Inhibitors of HIV‐1 Integrase LEDGF/p75 Interaction by Machine Learning Methods
Author(s) -
Li Yang,
Wu Yanbin,
Yan Aixia
Publication year - 2017
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.201600127
Subject(s) - integrase , integrase inhibitor , random forest , replication (statistics) , matthews correlation coefficient , support vector machine , computational biology , chemistry , viral replication , human immunodeficiency virus (hiv) , computer science , artificial intelligence , biology , antiretroviral therapy , virology , virus , viral load
HIV‐1 integrase (IN) is a promising target for anti‐AIDS therapy, and LEDGF/p75 is proved to enhance the HIV‐1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replication infection. In this work, 274 LEDGF/p75‐IN inhibitors were collected as the dataset. Support Vector Machine (SVM), Decision Tree (DT), Function Tree (FT) and Random Forest (RF) were applied to build several computational models for predicting whether a compound is an active or weakly active LEDGF/p75‐IN inhibitor. Each compound is represented by MACCS fingerprints and CORINA Symphony descriptors. The prediction accuracies for the test sets of all the models are over 70 %. The best model Model 3B built by FT obtained a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 81.08 % and 0.62 on test set, respectively. We found that the hydrogen bond and hydrophobic interactions are important for the bioactivity of an inhibitor.