P-Glycoprotein (P-gp), an efflux transporter, plays a crucial role in drug pharmacokinetic properties (ADME), and is critical for multidrug resistance (MDR) by mediating the active transport of anticancer drugs from the intracellular to the extracellular compartment. Here we reported an original database of 1273 molecules that are categorized into P-gp inhibitors and noninhibitors. The impact of various physicochemical properties on P-gp inhibition was examined. We then built the decision trees from a training set of 973 compounds using the recursive partitioning (RP) technique and validated by an external test set of 300 compounds. The best decision tree correctly predicted 83.5% of the inhibitors and 67.0% of the noninhibitors in the test set. Finally, we applied naive Bayesian categorization modeling to establish classifiers for P-gp inhibitors. The Bayesian classifier gave average correct prediction for 81.7% of 973 compounds in the training set with leave-one-out cross-validation procedure and 81.2% of 300 compounds in the test set. By establishing multiple decision trees and Bayesian classifiers, we evaluated the impact of molecular fingerprints on classification by the prediction accuracy for the test set, and we found that the inclusion of molecular fingerprints improves the prediction obviously. As an unsupervised learner without tuning parameters, the Bayesian classifier employing fingerprints highlights the important structural fragments favorable or unfavorable for P-gp transport, which provides critical information for designing new efficient P-gp inhibitors.

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Uptake of 18F-FDG on PET or PET/CT either before or after treatment has a promising value of both predicting survival outcomes for patients with cervical cancer and identifying patients for more aggressive treatment.