A kind of novel uniform monodispersed three-dimensional dendritic mesoporous silica nanospheres (3D-dendritic MSNSs) has been successfully synthesized for the first time. The 3D-dendritic MSNSs can have hierarchical mesostructure with multigenerational, tunable center-radial, and dendritic mesopore channels. The synthesis was carried out in the heterogeneous oil-water biphase stratification reaction system, which allowed the self-assembly of reactants taking place in the oil-water interface for one-pot continuous interfacial growth. The average pore size of each generation for the 3D-dendritic MSNSs can be adjusted from 2.8 to 13 nm independently, which can be controlled by the varied hydrophobic solvents and concentration of silica source in the upper oil phase. The thickness of each generation can be tuned from ∼ 5 to 180 nm as desired, which can be controlled by the reaction time and amount of silica source. The biphase stratification approach can also be used to prepare other core-shell and functional mesoporous materials such as Au nanoparticle@3D-dendritic MSNS and Ag nanocube@3D-dendritic MSNS composites. The 3D-dendritic MSNSs show their unique advantage for protein loading and releasing due to their tunable large pore sizes and smart hierarchical mesostructures. The maximum loading capacity of bovine β-lactoglobulin with 3D-dendritic MSNSs can reach as high as 62.1 wt % due to their large pore volume, and the simulated protein releasing process can be tuned from 24 to 96 h by flexible mesostructures. More importantly, the releasing rates are partly dependent on the hierarchical biodegradation, because the 3D-dendritic MSNSs with larger pore sizes have faster simulated biodegradation rates in simulated body fluid. The most rapid simulated biodegradation can be finished entirely in 24 h, which has been greatly shortened than two weeks for the mesoporous silica reported previously. As the inorganic mesoporous materials, 3D-dendritic MSNSs show excellent biocompatibility, and it would have a hopeful prospect in the clinical applications.

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.

Lined up water molecules: Artificial transmembrane channels from pillar[5]arene monomeric and dimeric derivatives have been prepared. Single-channel conductance measurements and isotope effect experiments under acidic conditions showed selective proton transport through the channels, which were mediated by water wires formed in the pillar[5]arene backbones (see picture).

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