Combining radial basis function neural network with genetic algorithm to QSPR modeling of adsorption on multi-walled carbon nanotubes surface

• Published in: Journal of molecular structure Vol. 1098; pp. 191 - 198
• Main Authors: Hassanzadeh, Zeinabe, Kompany-Zareh, Mohsen, Ghavami, Raouf, Gholami, Somayeh, Malek-Khatabi, Atefe
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2015
• Subjects: MWCNTs; QSPR; RBFN; wsGA-RBFN; QSPR; GA; MWCNTs; wsGA-RBFN; RBFN
• ISSN: 0022-2860; 1872-8014
• DOI: 10.1016/j.molstruc.2015.05.039

The configuring of a radial basis function neural network (RBFN) consists of optimizing the architecture and the network parameters (centers, widths, and weights). Methods such as genetic algorithm (GA), K-means and cluster analysis (CA) are among center selection methods. In the most of reports on RBFN modeling optimum centers are selected among rows of descriptors matrix. A combination of RBFN and GA is introduced for better description of quantitative structure-property relationships (QSPR) models. In this method, centers are not exactly rows of the independent matrix and can be located in any point of the samples space. In the proposed approach, initial centers are randomly selected from the calibration set. Then GA changes the locations of the initially selected centers to find the optimum positions of centers from the whole space of scores matrix, in order to obtain highest prediction ability. This approach is called whole space GA-RBFN (wsGA-RBFN) and applied to predict the adsorption coefficients (logk), of 40 small molecules on the surface of multi-walled carbon nanotubes (MWCNTs). The data consists of five solute descriptors [R, π, α, β, V] of the molecules and known as data set1. Prediction ability of wsGA-RBFN is compared to GA-RBFN and MLR models. The obtained Q2 values for wsGA-RBFN, GA-RBFN and MLR are 0.95, 0.85, and 0.78, respectively, which shows the merit of wsGA-RBFN. The method is also applied on the logarithm of surface area normalized adsorption coefficients (logKSA), of organic compounds (OCs) on MWCNTs surface. The data set2 includes 69 aromatic molecules with 13 physicochemical properties of the OCs. Thirty-nine of these molecules were similar to those of data set1 and the others were aromatic compounds included of small and big molecules. Prediction ability of wsGA-RBFN for second data set was compared to GA-RBF. The Q2 values for wsGA-RBFN and GA-RBF are obtained as 0.89 and 0.80, respectively. [Display omitted] •This paper proposes the wsGA-RBF method for the first time.•In wsGA-RBF, the centers of RBFN are not restricted to the rows of the input matrix and centers are free to move in the whole space.•The accuracy and predictive ability of the model was evaluated using internal and external procedures.•The domain of applicability of the model which indicates the area of reliable predictions was defined.•By the proposed method variable selection in QSPR studies, is not necessary.