Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors.
A new version of an ant colony optimization (ACO) algorithm has been proposed. A modified ACO algorithm is proposed to select variables in QSAR modeling and to predict inhibiting action of some diarylimidazole derivatives on cyclooxygenase (COX) enzyme. As a comparison to this method, the evolution algorithm (EA) was also tested. Experimental results have demonstrated that the modified ACO is a useful tool for variable selection that needs few parameters to be adjusted and converges quickly toward the optimal position.
Shen
Qi
Q
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Jiang
Jian-Hui
JH
Tao
Jing-Chao
JC
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D000465:Algorithms; D016861:Cyclooxygenase Inhibitors / Q000737:chemistry*; D007093:Imidazoles / Q000737:chemistry; D008954:Models, Biological; D015394:Molecular Structure; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship
D016861:Cyclooxygenase Inhibitors; D007093:Imidazoles