Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R²) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.