Design, Synthesis, Formulation, and Bioevaluation of Trisubstituted Triazines as Highly Selective mTOR Inhibitors for the Treatment of Human Breast Cancer.

The aberrant activation of the PI3K/mTOR signaling pathway is implicated in various human cancers. Thus, the development of inhibitors targeting mTOR has attracted considerable attention. In this study, we used a structure-based drug design strategy to discover a highly potent and kinase-selective mTOR inhibitor 24 (PT-88), which demonstrated an mTOR inhibitory IC50 value of 1.2 nM without obvious inhibition against another 195 kinases from the kinase profiling screening. PT-88 displayed selective inhibition against MCF-7 cells (IC50: 0.74 µM) with high biosafety against normal cells, in which autophagy induced by mTOR inhibition was implicated. After successful encapsulation in a lipodisc formulation, PT-88 demonstrated favorable pharmacokinetic and biosafety profiles and exerted a large antitumor effect in an MCF-7 subcutaneous bearing nude mice model. Our study shows the discovery of a highly selective mTOR inhibitor using a structure-based drug discovery strategy and provides a promising antitumor candidate for future study and development.

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors.

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.