The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug–target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of data and materials The proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523.

Cutaneous squamous cell carcinoma (cuSCC) comprises 15–20% of all skin cancers, accounting for over 700,000 cases in USA annually. Most cuSCC arise in association with a distinct precancerous lesion, the actinic keratosis (AK). To identify potential targets for molecularly targeted chemoprevention, here we perform integrated cross-species genomic analysis of cuSCC development through the preneoplastic AK stage using matched human samples and a solar ultraviolet radiation-driven Hairless mouse model. We identify the major transcriptional drivers of this progression sequence, showing that the key genomic changes in cuSCC development occur in the normal skin to AK transition. Our data validate the use of this ultraviolet radiation-driven mouse cuSCC model for cross-species analysis and demonstrate that cuSCC bears deep molecular similarities to multiple carcinogen-driven SCCs from diverse sites, suggesting that cuSCC may serve as an effective, accessible model for multiple SCC types and that common treatment and prevention strategies may be feasible.

Inhibition of endothelial cell proliferation and angiogenesis is emerging as an important strategy in cancer therapeutics. Kringle 5 (K5) of human plasminogen is a potent angiogenesis inhibitor. Previous studies have shown K5 exposure promotes caspase activity and apoptosis in endothelial cells. Here we report that K5 treatment evokes an autophagic response in endothelial cells that is specific and initiated even in the absence of nutritional stress. Endothelial cells exposed to K5 up-regulated Beclin 1 levels within a few hours. Furthermore, progressively increasing amounts of antiapoptotic Bcl-2 were found to be complexed with Beclin 1, although total levels of Bcl-2 remained unchanged. Prolonged exposure to K5 ultimately led to apoptosis via mitochondrial membrane depolarization and caspase activation in endothelial cells. Knocking down Beclin 1 levels by RNA interference decreased K5 induced autophagy but accelerated K5-induced apoptosis. These studies suggest that interfering with the autophagic survival response can potentiate the antiangiogenic effects of Kringle 5 in endothelial cells.

Endostatin is a well-characterized endogenous inhibitor of angiogenesis that affects cell proliferation and migration by inhibiting integrin and Wnt-mediated signalling pathways. Here, we show that endothelial cells treated with native and P125A-endostatin activate autophagy. Because autophagy can either be protective or induce programmed cell death, experiments were carried out to understand the signalling pathways leading to autophagy in endothelial cells. P125A-endostatin treatment increased the levels of Beclin 1, a crucial molecule in vesicle nucleation and autophagy. The treatment also reduced the levels of Bcl-2, Bcl-xL and β-catenin; however, progressively increasing amounts of Bcl-2 and Bcl-xL were found to be complexed with Beclin 1. Increased β-catenin and Wnt-mediated signalling reduced Beclin 1 levels and rescued endothelial cells from endostatin-induced autophagy. Finally, knocking down Beclin 1 levels by RNA interference decreased autophagy and accelerated caspase activation in endostatin-treated cells. These studies suggest that endothelial cells may initiate autophagy as a survival response to limit the effects of angiogenesis inhibitors. Thus, interfering with autophagy can potentiate the effects of endostatin by promoting a switch to apoptosis.