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A Genetic Algorithm- Back Propagation Artificial Neural Network Model to Quantify the Affinity of Flavonoids Toward P-Glycoprotein

[ Vol. 17 , Issue. 2 ]


Jibin Shen, Ying Cui, Jun Gu, Yaxiao Li and Lingzhi Li   Pages 162 - 172 ( 11 )


Flavonoids, the most diverse class of plant secondary metabolites, exhibit high affinity toward the purified cytosolic NBD2(C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationships (QSARs) models were developed using back-propagation artificial neural networks (BPANN) and multiple linear regression (MLR). Molecular descriptors were calculated using PaDELDescriptor, and the number of descriptors was then reduced using a genetic algorithm (GA) and stepwise regression. The MLR (R2=0.855, q2=0.8138, Rext 2=0.6916), 14-3-1 BPANN (R2=0.8514, q2=0.7695, Rext 2=0.8142), 14-4-1 BPANN (R2=0.9199, q2=0.7733, Rext 2 =0.8731), and 14-5-1 BPANN (R2=0.8660, q2=0.7432, Rext 2 =0.8292) models all showed good robustness. While BPANN models exceeded significantly MLR in predictable performance for their flexible characters, could be used to predict the affinity of flavonoids for P-gp and applied in further drug screening.


Back-propagation artificial neural networks, flavonoids, genetic algorithm, P-glycoprotein.


Department of Medicine Chemistry, Logistics College of Chinese People’s Armed Police Forces, China.

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