Enhancement of binary QSAR analysis by a GA-based variable selection method.
Binary quantitative structure-activity relationship (QSAR) is an approach for the analysis of high throughput screening (HTS) data by correlating structural properties of compounds with a "binary" expression of biological activity (1 = active and 0 = inactive) and calculating a probability distribution for active and inactive compounds in a training set. Successfully deriving a predictive binary or any QSAR model largely depends on the selection of a preferred set of molecular descriptors that can capture the chemico-biological interaction for a particular biological target. In this study, a genetic algorithm (GA) was applied as a variable selection method in binary QSAR analysis. This GA-based variable selection method was applied to the analysis of three diverse sets of compounds, estrogen receptor (ER) ligands, carbonic anhydrase II inhibitors, and monoamine oxidase (MAO) inhibitors. Out of a variable pool of 150 molecular descriptors, predictive binary QSAR models were obtained for all three sets of compounds within a reasonable number of GA generations. The results indicate that the GA is a very effective variable selection approach for binary QSAR analysis.
Gao
Hua
H
Computer-Aided Drug Discovery, Pharmacia, Kalamazoo, MI 49007, USA. hua.gao@pharmacia.com
Lajiness
Michael S
MS
Van Drie
John
J
D000465:Algorithms; D024402:Carbonic Anhydrase II / Q000037:antagonists & inhibitors*; D004967:Estrogens / Q000032:analysis*; D008024:Ligands; D015394:Molecular Structure; D008996:Monoamine Oxidase Inhibitors / Q000032:analysis*; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen
D004967:Estrogens; D008024:Ligands; D008996:Monoamine Oxidase Inhibitors; D011960:Receptors, Estrogen; D024402:Carbonic Anhydrase II