Jiye Wang

The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more difficult to manually extract useful medicinal chemistry knowledge. To this end, we introduced IDL-PPBopt, a computational strategy for predicting and optimizing the plasma protein binding (PPB) property based on an interpretable deep learning method. At first, a curated PPB data set was used to construct an interpretable deep learning model, which showed excellent predictive performance with a root mean squared error of 0.112 for the entire test set. Then, we designed a detection protocol based on the model and Wilcoxon test to identify the PPB-related substructures (named privileged substructures, PSubs) for each molecule. In total, 22 general privileged substructures (GPSubs) were identified, which shared some common features such as nitrogen-containing groups, diamines with two carbon units, and azetidine. Furthermore, a series of second-level chemical rules for each GPSub were derived through a statistical test and then summarized into substructure pairs. We demonstrated that these substructure pairs were equally applicable outside the training set and accordingly customized the structural modification schemes for each GPSub, which provided alternatives for the optimization of the PPB property. Therefore, IDL-PPBopt provides a promising scheme for the prediction and optimization of the PPB property and would be helpful for lead optimization of other pharmacokinetic properties.

NAD(P)H:quinone oxidoreductase 1 (NQO1) has been shown to be a potential therapeutic target for various human diseases, such as cancer and neurodegenerative disorders. Recent advances in computational methods, especially network-based methods, have made it possible to identify novel compounds for a target with high efficiency and low cost. In this study, we designed a workflow combining network-based methods and identification of privileged substructures to discover new compounds targeting NQO1 from a natural product library. According to the prediction results, we purchased 56 compounds for experimental validation. Without the assistance of privileged substructures, 31 compounds (31/56 = 55.4%) showed IC₅₀ < 100 μM, and 11 compounds (11/56 = 19.6%) showed IC₅₀ < 10 μM. With the assistance of privileged substructures, the two success rates were increased to 61.8 and 26.5%, respectively. Seven natural products further showed inhibitory activity on NQO1 at the cellular level, which may serve as lead compounds for further development. Moreover, network analysis revealed that osthole may exert anticancer effects against multiple cancer types by inhibiting not only carbonic anhydrases IX and XII but also NQO1. Our workflow and computational methods can be easily applied in other targets and become useful tools in drug discovery and development.

Identification of drug–pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug–pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug–pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug–pathway associations based on substructure–drug–pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug–pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug–pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.