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Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug–drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

 

Abstract Long bone growth requires the precise control of chondrocyte maturation from proliferation to hypertrophy during endochondral ossification, but the bioenergetic program that ensures normal cartilage development is still largely elusive. We show that chondrocytes have unique glucose metabolism signatures in these stages, and they undergo bioenergetic reprogramming from glycolysis to oxidative phosphorylation during maturation, accompanied by an upregulation of the pentose phosphate pathway. Inhibition of either oxidative phosphorylation or the pentose phosphate pathway in murine chondrocytes and bone organ cultures impaired hypertrophic differentiation, suggesting that the appropriate balance of these pathways is required for cartilage development. Insulin-like growth factor 2 (IGF2) deficiency resulted in a profound increase in oxidative phosphorylation in hypertrophic chondrocytes, suggesting that IGF2 is required to prevent overactive glucose metabolism and maintain a proper balance of metabolic pathways. Our results thus provide critical evidence of preference for a bioenergetic pathway in different stages of chondrocytes and highlight its importance as a fundamental mechanism in skeletal development. 

Abstract We present the detection of neutral helium at 10833 Å in the atmosphere of WASP-52b and tentative evidence of helium in the atmosphere of the grazing WASP-177b, using high-resolution observations acquired with the NIRSPEC instrument on the Keck II telescope. We detect excess absorption by helium in WASP-52b’s atmosphere of 3.44% ± 0.31% (11 σ ), or equivalently 66 ± 5 atmospheric scale heights. This absorption is centered on the planet’s rest frame (Δ v = 0.00 ± 1.19 km s −1 ). We model the planet’s escape using a 1D Parker wind model and calculate its mass-loss rate to be ∼1.4 × 10 11 g s −1 , or equivalently 0.5% of its mass per gigayear. For WASP-177b, we see evidence for redshifted (Δ v = 6.02 ± 1.88 km s −1 ) helium-like absorption of 1.28% ± 0.29% (equal to 23 ± 5 atmospheric scale heights). However, due to residual systematics in the transmission spectrum of similar amplitude, we do not interpret this as significant evidence for He absorption in the planet’s atmosphere. Using a 1D Parker wind model, we set a 3 σ upper limit on WASP-177b’s escape rate of 7.9 × 10 10 g s −1 . Our results, taken together with recent literature detections, suggest the tentative relation between XUV irradiation and He i absorption amplitude may be shallower than previously suggested. Our results highlight how metastable helium can advance our understanding of atmospheric loss and its role in shaping the exoplanet population. 

3D printing technology is able to produce personalized artificial substitutes for patients with damaged menisci. However, there is a lack of thorough understanding of 3D printing-enabled (3DP-enabled) meniscus transplantation and its long-term advantages over traditional transplantation. To help health care stakeholders and patients assess the value of 3DP-enabled meniscus transplantation, this study compares the long-term cost and risk of this new paradigm with traditional transplantation by simulation. Pathway models are developed to simulate patients’ treatment process during a 20-year period, and a Markov process is used to model the state transitions of patients after transplantation. A sensitivity analysis is also conducted to show the effect of quality of 3D-printed meniscus on model outputs. The simulation results suggest that the performance of 3DP-enabled meniscus transplantation depends on quality of 3D-printed meniscus. The conclusion of this study is that 3DP-enabled meniscus transplantation has many advantages over traditional meniscus transplantation, including a minimal waiting time, perfect size and shape match, and potentially lower cost and risk in the long term.