title: Quantitative structure-lambda(max) relationship study on flavones by heuristic method and radial basis function neural network.
abstract: QSPR models for the prediction of UV maximum absorption wavelength (lambda(max)) of 69 flavones were developed based on their structures alone. A six-descriptor linear correlation by heuristic method (HM) and a nonlinear model using radial basis function neural network (RBFNN) approach were reported. The statistical parameters provided by the HM model (R2=0.961, F=207.820, RMS=6.555 for the training set and R2=0.967, F=293.218, RMS=7.176 for the test set) and the RBFNN model (R2=0.971, F=1826.086, RMS=5.350 for the training set, and R2=0.978, F=452.512, RMS=5.722 for the test set) indicated satisfactory stability and predictive ability. The descriptors appearing in these models are discussed. This QSPR approach is suitable for the prediction of maximum absorption wavelength of flavones, and can contribute to a better understanding of structural factors of the organic compounds responsible for it.
authors: Department of Applied Chemistry, Yantai University, No. 32 Qingquan Road, Yantai 264005, PR China. liuht-ytu@163.com
authors: Huitao
authors: H
authors: Liu
authors: Yingying
authors: Y
authors: Wen
authors: Feng
authors: F
authors: Luan
authors: Yuan
authors: Y
authors: Gao
authors: Xiuyong
authors: X
authors: Li
mesh_terms: D000042:Absorption; D047309:Flavones / Q000737:chemistry*; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D013056:Spectrophotometry, Ultraviolet / Q000379:methods*
chemical_list: D047309:Flavones