Identifying natural products for gastric cancer treatment through pharmacophore creation, 3D QSAR, virtual screening, and molecular dynamics studies.

BACKGROUND: Gastric cancer (GC) is known as the fourth leading cause of cancer-related death and the fifth major cancer in the world, and this is a serious threat to general health all over the world. The lack of early detection markers results in a belated diagnosis, i.e. the final stages, which could be associated with the ineffectiveness of the treatment strategies, and naturally, it leads to poor prognosis. Even though a variety of treatments have been developed, there is a trend of studying traditional medicinal plants, due to the worrying side effect of drugs available in the market. METHODS: In this study, pharmacophore generation and 3D-QSAR model were created using 50 compounds with anti-gastric cancer activity (with IC50 had been reported in the previous studies). RESULTS: Based on three of the best pharmacophoric hypotheses, virtual screening was performed to discover the top anti-gastric cancer compounds from a database of 183,885 compounds. The selected compounds were used for molecular docking with three protein receptors 7BKG, 4F5B, and 4ZT1 to investigate the intermolecular interactions between these ligands and receptors. Finally, 21 lead compounds with the highest amount of docking score ranging from - 13.366 to -6.404 kcal/mol were selected, and then the ADME/Tox properties of these compounds were calculated. All these compounds have a fitness score above 1.8, a molecular weight of less than 500 g/mol, hydrogen bond donors up to 3, hydrogen bond acceptors up to 8.50, and logP of 1.013 to 4.174. Finally, molecular dynamic simulations for top-scoring ligand-receptor complexes were investigated. CONCLUSION: These selected lead compounds have the most anti-gastric cancer effects among the 183,885 compounds in the database. Therefore, lead compounds might be considered for gastric cancer therapy in future studies.

Social stratification in networks: insights from co-authorship networks.

It has been observed that real-world social networks often exhibit stratification along economic or other lines, with consequences for class mobility and access to opportunities. With the rise in human interaction data and extensive use of online social networks, the structure of social networks (representing connections between individuals) can be used for measuring stratification. However, although stratification has been studied extensively in the social sciences, there is no single, generally applicable metric for measuring the level of stratification in a network. In this work, we first propose the novel Stratification Assortativity (StA) metric, which measures the extent to which a network is stratified into different tiers. Then, we use the StA metric to perform an in-depth analysis of the stratification of five co-authorship networks. We examine the evolution of these networks over 50 years and show that these fields demonstrate an increasing level of stratification over time, and, correspondingly, the trajectory of a researcher's career is increasingly correlated with her entry point into the network.