The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 μg m-3 for the former and R2 = 0.65 and RMSE = 15.69 μg m-3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.

Background: To explore the modulatory effects and mechanism of secretory clusterin (sCLU) on cancer stem cell (CSC) properties in hepatocellular carcinoma (HCC). Methods: The effects of sCLU repression or overexpression on chemoresistance, migration, invasion, and tumor growth were detected by MTT, wound healing, transwell assays, and xenograft assay, respectively. The tumor sphere assay was performed to evaluate the self-renewal ability of HCC cells. In addition, the molecular regulation between sCLU and AKT/GSK-3β/β-catenin axis in HCC cells were discovered by western blotting, quantitative real-time PCR (qRT-PCR), and immunofluorescence. The expression status of sCLU and β-catenin in HCC tissues were investigated by immunohistochemistry. Results: Knockdown or overexpressing sCLU remarkably inhibited or promoted the chemoresistance against sorafenib/doxorubicin, metastasis, and tumor growth of HCC cells, respectively. HepG2 and HCCLM3-derived spheroids showed higher expression of sCLU than that in attached cells. Additionally, repressing sCLU impaired the self-renewal capacity of HCC cells and CSC-related chemoresistance while overexpression of sCLU enhanced these CSC properties. Knockdown or overexpression of sCLU inhibited or increased the expressions of β-catenin, cyclinD1, MMP-2 and MMP-9, and the phosphorylation of AKT or GSK3β signaling, respectively. However, LiCl or LY294002 abrogated the effects mediated by sCLU silencing or overexpression on chemoresistance, metastasis, and CSC phenotype. Furthermore, co-expression of sCLU and β-catenin in HCC tissues indicated poor prognosis of HCC patients. Conclusions: Taken together, the oncogenic sCLU might promote CSC phenotype via activating AKT/GSK3β/β-catenin axis, suggesting that sCLU was a potential molecular-target for HCC therapy.

In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradually been used in drug research with strong computational capability and is further applied in anti-HBV drug screening, thus facilitating a reliable drug screening process. However, the lack of structural information in traditional compound analysis is an important hurdle for unsatisfactory efficiency in drug screening. Here, a natural language processing technique was adopted to analyze compound simplified molecular input line entry system strings. By using the targeted optimized word2vec model for pretraining, we can accurately represent the relationship between the compound and its substructure. The machine learning model based on training results can effectively predict the inhibitory effect of compounds on HBV and liver toxicity. The reliability of the model is verified by the results of wet-lab experiments. In addition, a tool has been published to predict potential compounds. Hence, this article provides a new perspective on the prediction of compound properties for anti-HBV drugs that can help improve hepatitis B diagnosis and further develop human health in the future.