title: Optimized partition of minimum spanning tree for piecewise modeling by particle swarm algorithm. QSAR studies of antagonism of angiotensin II antagonists.
abstract: In quantitative structure-activity relationship (QSAR) modeling, when compounds in a training set exhibit a significant structural distinction between each other, in particular when chemicals of biological interest interacting on the receptor involve a different mechanism, it might be difficult to construct a single linear model for the whole population of compounds of interest with desired residuals. Developing a piecewise linear local model can be effective to circumvent the aforementioned problem. In this paper, piecewise modeling by the particle swarm optimization (PMPSO) approach is applied to QSAR study. The minimum spanning tree is used for clustering all compounds in the training set to form a tree, and the modified discrete PSO is applied to divide the tree to find satisfactory piecewise linear models. A new objective function is formulated for searching the appropriate piecewise linear models. The proposed PMPSO algorithm was used to predict the antagonism of angiotensin II. The results demonstrated that PMPSO is useful for improvement of the performance of regression models.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Shen
authors.forename: Jian-Hui
authors.initials: JH
authors.lastname: Jiang
authors.forename: Chen-Xu
authors.initials: CX
authors.lastname: Jiao
authors.forename: Shuang-Yan
authors.initials: SY
authors.lastname: Huan
authors.forename: Guo-li
authors.initials: GL
authors.lastname: Shen
authors.forename: Ru-Qin
authors.initials: RQ
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000804:Angiotensin II / Q000037:antagonists & inhibitors*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000804:Angiotensin II