title: Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.
abstract: 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.
authors.affiliation: 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.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Insititute of Medicinal Biotechnology Chinese Academy of Medical Sciences, 1 Tian Tan Xi Li, Beijing, 100050, P. R. China.
authors.forename: Yanbin
authors.initials: Y
authors.lastname: Wu
authors.affiliation: 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.
authors.forename: Aixia
authors.initials: A
authors.lastname: Yan
mesh_terms: D003198:Computer Simulation; D019427:HIV Integrase / Q000737:chemistry* / Q000378:metabolism; D019428:HIV Integrase Inhibitors / Q000737:chemistry*; D006801:Humans; D036341:Intercellular Signaling Peptides and Proteins / Q000737:chemistry* / Q000378:metabolism; D000069550:Machine Learning; D008958:Models, Molecular; D008968:Molecular Conformation; D015394:Molecular Structure; D011485:Protein Binding; D012372:ROC Curve; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship
chemical_list: D019428:HIV Integrase Inhibitors; D036341:Intercellular Signaling Peptides and Proteins; C119380:lens epithelium-derived growth factor; D019427:HIV Integrase; C514801:p31 integrase protein, Human immunodeficiency virus 1
keywords: Classification model; Extended connectivity fingerprints (ECFP_4); HIV-1 integrase (IN) LEDGF/p75 inhibitor; Machine learning method