✍ Scribed by Bahram Hemmateenejad; Ramin Miri; Mohammad A. Safarpour; Ahmad R. Mehdipour
No coin nor oath required. For personal study only.
A genetic algorithm‐based artificial neural network model has been developed for the accurate prediction of the blood–brain barrier partitioning (in log__BB__ scale) of chemicals. A data set of 123 log__BB__ (115 old molecules and 8 new molecules) of a diverse set of chemicals was chosen in this study. The optimum 3D geometry of the molecules was estimated by the ab initio calculations at the level of RHF/STO‐3G, and consequently, different electronic descriptors were calculated for each molecule. Indeed, log__P__ as a measure of hydrophobicity and different topological indices were also calculated. A three‐layered artificial neural network with backpropagation of an error‐learning algorithm was employed to process the nonlinear relationship between the calculated descriptors and log__BB__ data. Genetic algorithm was used as a feature selection method to select the most relevant set of descriptors as the input of the network. Modeling of the log__BB__ data by the only quantum descriptors produced a 5:4:1 ANN structure with RMS error of validation and crossvalidation equal to 0.224 and 0.227, respectively. Better nonlinear model (RMS~V~ and RMS~CV~ equals to 0.097 and 0.099, respectively) was obtained by the incorporation of the log__P__ and the principal components of the topological indices to electronic descriptors. The ultimate performances of the models were obtained by the application of the models to predict the log__BB__ of 23 molecules that did not have contribution in the steps of model development. The best model produced RMS error of prediction 0.140, and could predict about 98% of variances in the log__BB__ data. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1125–1135, 2006
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