The discovery of drugs for diseases of the central nervous system (CNS) faces high attrition rates in clinical trials. Neural diseases are extremely complex in nature and typically associated with multiple drug targets. A conception of multi-target directed ligands (MTDL), widely applied to the discovery of cancer pharmaceuticals, may be a perspective solution for CNS diseases. Special bioinformatics approaches have been developed which can assist the medicinal chemists in identification and structural optimization of MTDL. In this review, we analyze the current status of the development of multitarget approaches in quantitative structure-activity relationships (mt-QSAR) for CNS drug discovery; and describes applications of multi-target approaches in molecular modelling (which can be called mt-MM), as well as perspectives for multi-target approaches in bioinformatics in relation to Alzheimer’s disease.

Introduction An Order in Increasing Size of Silyl Groups Comparison of Organosilyl Groups in Substrates Comparison of the Influence Resulting from Hydrogen Atom and Alkyl Groups versus Silyl Groups Reactivity of Silicon‐Containing Reagents Solvolysis of Various Organosilanes Change of Physical, Chemical and Spectroscopic Properties by Introduction of Silyl Groups Conclusion Acknowledgment

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Artificial neural networks application in science and techonology has begun during twentieth century. This biophysical and biomimetic phenomena is based on extensive research which have led to understanding how neural as a living organism nerve system basic element processes signals by a simple algorithm; the input signals are massively parallel processed, and the output presents the superposition of all parallel processed signals. Artifical neural networks which are based on these principles are useful for solving various problems as pattern recognition, clustering, functional optimization. This research analyzed termophysical parameters at samples based on Murata powders and consolidated by sintering process. Among different physical properties we applied out neural network approach on grain sizes distribution as a function of sintering temperature (T), (from 1190-1370?C). In this paper, we continue to apply neural networks to prognose structural and thermophysical parameters. For consolidation sintering process is very important to prognoze and design many parameters but especially thermal like temperature, to avoid long and even wrong experiments which are waisting the time and materials and energy as well. By this ANN method we indeed provide the most efficient procedure in projecting the mentioned parameters and provide successful ceremics samples production. This is very helpful in prediction and designing the microstructure parameters important for advance microelectronic further miniturisation development. This is a quite original novelty for real microstructure projecting especially on the phenomena within the thin films coating around the grains what opens new prospectives in advance fractal microelectronics.

In their editorial, Reuben Jih‐Ru Hwu and Theresa Kueckmann reflect on the objective of the 18th Asian Chemical Congress (ACC). The ACC is a biennial event organized by members of the Federation of Asian Chemical Societies (FACS), an official supporting organization for the publication of the three ACES journals Chemistry‐An Asian Journal, Asian Journal of Organic Chemistry, and ChemNanoMat. This special issue showcases the high quality of the 2019 ACC, featuring contributions by Keynote and Invited Speakers.