A QSAR study of HIV protease inhibitors using theoretical descriptors.
This paper reports the development of quantitative structure-activity relationship (QSAR) models for a set of 170 chemicals using mathematical descriptors which can be calculated directly from molecular structure without the input of any other experimental data. The calculated descriptors include topostructural (TS), topochemical (TC), and quantum chemical (QC). Because the situation is rank deficient i.e. the number of independent variables (descriptors) is larger than the number of compounds, three robust linear statistical modeling methods capable of handling such situations, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR) were used for QSAR formulation. Results show that PLS and RR gave better q2 values as compared to the PCR method. Of the three classes of descriptors, the TC indices were the best predictors of anti-HIV activity and the QC indices were the least effective.
Basak
Subhash C
SC
Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN 55811, USA. sbasak@nrri.umn.edu
Mills
Denise
D
Garg
Rajni
R
Bhhatarai
Barun
B
D017320:HIV Protease Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D006801:Humans; D016018:Least-Squares Analysis; D008958:Models, Molecular; D015233:Models, Statistical; D015394:Molecular Structure; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
D017320:HIV Protease Inhibitors