New Cyclization Route to the Skeletons of Lennoxamine and Chilenine

A new cyclization route, triggered by epoxide opening, has been performed to provide the key intermediates for isoindolobenzapine alkaloids, lennoxamine and chilenine. The epoxide was prepared by the Stille reaction using vinyltributylstannane and the following dioxirane treatment. Cyclization under the treatment of BF₃ · OEt₂ provided an azepine moiety, and the oxidative cyclization toward the known precursor for the alkaloids has been achieved by reaction with a stoichiometric amount of Pd(OAc)₂. This formal synthesis suggests a new route to the alkaloids.     

Reaction Behaviors of S-, O-, and N-containing Aliphatic Molecules with a Propyl Moiety on Ge(100) Surface

The reaction pathways of 1-propanethiol, 1-propanol, and propylamine molecules, containing a propyl moiety, on a Ge(100) surface were investigated using high-resolution photoemission spectroscopy (HRPES) experiments and density functional theory (DFT) calculations. Upon analysis of the HRPES data, the adsorption of 1-propanethiol and 1-propanol was found to occur through a dissociation reaction, whereas that of propylamine took place via N dative bonding at room temperature. On the basis of our DFT results, adsorption geometries and transition states for each of these molecules on the Ge(100) surface were confirmed. Systematic studies of S-, O-, and N-containing molecules, composed of an identical propyl moiety, on the Ge(100) surface provide insight into the adsorption mechanism of aliphatic molecules containing alkyl chains on the Ge(100) surface.     

Electro‐optic switching with liquid crystal graphene

Graphene oxide (GO) particles in aqueous dispersions can form liquid crystal (LC) phases at extremely low concentrations due to the extremely high aspect ratio of the flakes and noticeably, they possess an extremely large Kerr coefficient attractive for low power consumption electro‐optic devices. Reduced graphene does not easily form LC phases in water due to its hydrophobic nature but here we show that stable dispersions of reduced graphene oxide can be realized with surfactants and that they exhibit birefringence upon shearing as well as under application of electric fields. The performance of the system is largely superior to GO LC possessing longer time stability and drastically improved electro‐optic properties with an induced birefringence twice as large at the same field strength thanks to the almost recovery of graphene properties upon reduction. (© 2016 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

Aqueous‐Solution Route to Zinc Telluride Films for Application to CO2 Reduction

As a photocathode for CO₂ reduction, zinc‐blende zinc telluride (ZnTe) was directly formed on a Zn/ZnO nanowire substrate by a simple dissolution–recrystallization mechanism without any surfactant. With the most negative conduction‐band edge among p‐type semiconductors, this new photocatalyst showed efficient and stable CO formation in photoelectrochemical CO₂ reduction at ?0.2–?0.7 V versus RHE without a sacrificial reagent.     

Pd‐Catalyzed Cycloisomerization of 4‐Aza‐1,6‐enynes to 3‐Aza‐bicyclo[4.1.0]hept‐2‐enes

A method for the synthesis of bicyclo[4.1.0]heptenes from 1,6‐enynes through Pd‐catalyzed cycloisomerization has been developed. N‐ and O‐tethered 1,6‐enynes were successfully transformed to their corresponding 3‐aza‐ and 3‐oxabicyclo[4.1.0]heptenes in reasonable‐to‐high yields using the catalysts [PdCl₂(CH₃CN)₂]/P(OPh)₃ or [Pd(maleimidate)₂(PPh₃)₂] in toluene. The computational calculations using density functional theory indicate that [PdCl₂{P(OPh)₃}] in the oxidation state Pd^II acts as the active catalyst species for the formation of 3‐azabicyclo[4.1.0]heptenes through 6‐endo‐dig cyclization.     

Bacterial cellulose/triethanolamine based ion-conducting membranes

New bacterial cellulose (BC)–triethanolamine (TEA) ion-conducting membranes have been prepared and characterized. The samples were obtained by soaking BC membranes in triethanolamine aqueous solutions and drying. The scanning electron microscopy pictures revealed that the incorporation of TEA in BC membranes covers the cellulose microfibrils. Raman spectra exhibited BC and TEA characteristic group frequencies and thermal analysis evidenced an influence of TEA content on the sample thermal stability. The ion-conductivity as a function of the temperature showed an Arrhenius behavior increasing from 1.8 × 10^?5 S/cm at room temperature to 7.0 × 10^?4 S/cm at 80 °C for the BC–TEA 1 M sample.     

Luminous Butterflies: Efficient Exciton Harvesting by Benzophenone Derivatives for Full‐Color Delayed Fluorescence OLEDs

Butterfly‐shaped luminescent benzophenone derivatives with small energy gaps between their singlet and triplet excited states are used to achieve efficient full‐color delayed fluorescence. Organic light‐emitting diodes (OLEDs) with these benzophenone derivatives doped in the emissive layer can generate electroluminescence ranging from blue to orange–red and white, with maximum external quantum efficiencies of up to 14.3 %. Triplet excitons are efficiently harvested through delayed fluorescence channels.     

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.