QSAR study of angiotensin II antagonists using robust boosting partial least squares regression.
In the current study, robust boosting partial least squares (RBPLS) regression has been proposed to model the activities of a series of 4H-1,2,4-triazoles as angiotensin II antagonists. RBPLS works by sequentially employing PLS method to the robustly reweighted versions of the training compounds, and then combing these resulting predictors through weighted median. In PLS modeling, an F-statistic has been introduced to automatically determine the number of PLS components. The results obtained by RBPLS have been compared to those by boosting partial least squares (BPLS) repression and partial least squares (PLS) regression, showing the good performance of RBPLS in improving the QSAR modeling. In addition, the interaction of angiotensin II antagonists is a complex one, including topological, spatial, thermodynamic and electronic effects.
Zhou
Yan-Ping
YP
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Cai
Chen-Bo
CB
Huan
Shi
S
Jiang
Jian-Hui
JH
Wu
Hai-Long
HL
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D047228:Angiotensin II Type 1 Receptor Blockers / Q000737:chemistry* / Q000494:pharmacology*; D057911:Angiotensin Receptor Antagonists; D016018:Least-Squares Analysis; D015233:Models, Statistical; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
D047228:Angiotensin II Type 1 Receptor Blockers; D057911:Angiotensin Receptor Antagonists