Piecewise hypersphere modeling by particle swarm optimization in QSAR studies of bioactivities of chemical compounds.
As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.
Lin
Wei-Qi
WQ
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Jiang
Jian-Hui
JH
Shen
Qi
Q
Wu
Hai-Long
HL
Shen
Guo-Li
GL
Yu
Ru-Qin
RQ
D000804:Angiotensin II / Q000037:antagonists & inhibitors; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D066246:ErbB Receptors / Q000037:antagonists & inhibitors; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship
D004791:Enzyme Inhibitors; D000804:Angiotensin II; D066246:ErbB Receptors