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 (R(2)=0.855, q(2)=0.8138, Rext(2)=0.6916), 14-3-1 BPANN (R(2)=0.8514, q(2)=0.7695, Rext (2)=0.8142), 14-4-1 BPANN(R(2)=0.9199, q(2)=0.7733, Rext(2) =0.8731), and 14-5-1 BPANN (R(2)=0.8660, q(2)=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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.