Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neuralnetwork framework called self-attention-based message-passing neural network (SAMPN) to study the relationship between chemical properties and structures in an interpretable way. The main advantages of SAMPN are that it directly uses chemical graphs and breaks the black-box mold of many machine/deep learning methods. Specifically, its attention mechanism indicates the degree to which each atom of the molecule contributes to the property of interest, and these results are easily visualized. Further, SAMPN outperforms random forests and the deep learning framework MPN from Deepchem. In addition, another formulation of SAMPN (Multi-SAMPN) can simultaneously predict multiple chemical properties with higher accuracy and efficiency than other models that predict one specific chemical property. Moreover, SAMPN can generate chemically visible and interpretable results, which can help researchers discover new pharmaceuticals and materials. The source code of the SAMPN prediction pipeline is freely available at Github (https ://githu b.com/tbwxm u/SAMPN ).

Injectable thermogel to deliver chemotherapeutics in a minimally invasive manner and to achieve their long term sustained release at tumor sites to minimize side effects is attractive for chemotherapy and precision medicine, but its rational design remains a challenge. In this work, a copolymer with natural biodegradable poly[(R)-3-hydroxybutyrate] (PHB), hydrophilic poly(ethylene glycol), and temperature sensitive poly(propylene glycol) blocks linked by urethane linkages is designed to show thermogelling characteristics which are beneficial for minimally invasive injection and safe degradation. This thermogelling polymer possesses in vitro biocompatibility with very low cyto-toxicity in HEK293 cells. Furthermore, it is able to form the gel to achieve the controllable release of paclitaxel (PTX) and doxorubicin (DOX) by adjusting polymer concentrations. A rodent model of hepatocarcinoma has been performed to demonstrate the in vivo applications of this PHB-based thermogel. The drug-loaded thermogel has been intratumorally injected and both PTX-loaded and DOX-loaded thermogel have significantly slowed down tumor growth. This work represents the first time that injectable PHB thermogels have possessed good controllable release effect of chemotherapeutics against the in vivo model of tumors and will benefit various applications, including on-demand drug delivery and personalized medicine.

This work represents the first time that poly(PEG/PPG/PLA urethane) has been used for the delivery of drugs to tumours in vivo and the encouraging results point to the potential for further development of this thermogel platform for anti-cancer applications.

Dysregulation of β-catenin turnover due to mutations of its regulatory proteins including APC and p53 is implicated in the pathogenesis of cancer. Thus, intensive effort is being made to search for alternative approaches to reduce abnormally activated β-catenin in cancer cells. Nur77, an orphan member of the nuclear receptor superfamily, plays a role in the growth and apoptosis of cancer cells. Here, we reported that Nur77 could inhibit transcriptional activity of β-catenin by inducing β-catenin degradation via proteasomal degradation pathway that is GSK3β and Siah-1 independent. Nur77 induction of β-catenin degradation required both the N-terminal region of Nur77, which was involved in Nur77 ubiquitination, and the C-terminal region, which was responsible for β-catenin binding. Nur77/ΔDBD, a Nur77 mutant lacking its DNA-binding domain, resided in the cytoplasm, interacted with β-catenin, and induced β-catenin degradation, demonstrating that Nur77-mediated β-catenin degradation was independent of its DNA-binding and transactivation and might occur in the cytoplasm. In addition, we reported our identification of two digitalis-like compounds (DLCs), H-9 and ATE-i2-b4, which potently induced Nur77 expression and β-catenin degradation in SW620 colon cancer cells expressing mutant APC protein in vitro and in animals. DLC-induced Nur77 protein was mainly found in the cytoplasm, and inhibition of Nur77 nuclear export by the CRM1-dependent nuclear export inhibitor leptomycin B or Jun N-terminal kinase inhibitor prevented the effect of DLC on inducing β-catenin degradation. Together, our results demonstrate that β-catenin can be degraded by cytoplasmic Nur77 through their interaction and identify H-9 and ATE-i2-b4 as potent activators of the Nur77-mediated pathway for β-catenin degradation.