Catalytic gas-phase glycerol processing over SiO2-, Cu-, Ni-and Fe-supported Au nanoparticles

In this study, we investigated different metal pairings of Au nanoparticles (NPs) as potential catalysts for glycerol dehydration for the first time. All of the systems preferred the formation of hydroxyacetone (HYNE). Although the bimetallics that were tested, i.e., Au NPs supported on Ni, Fe and Cu appeared to be more active than the Au/SiO2 system, only Cu supported Au NPs gave high conversion (ca. 63%) and selectivity (ca. 70%) to HYNE

The Forty-Sixth Euro Congress on Drug Synthesis and Analysis: Snapshot

The 46th EuroCongress on Drug Synthesis and Analysis (ECDSA-2017) was arranged within the celebration of the 65th Anniversary of the Faculty of Pharmacy at Comenius University in Bratislava, Slovakia from 5-8 September 2017 to get together specialists in medicinal chemistry, organic synthesis, pharmaceutical analysis, screening of bioactive compounds, pharmacology and drug formulations; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topic of the conference, Drug Synthesis and Analysis, meant that the symposium welcomed all pharmacists and/or researchers (chemists, analysts, biologists) and students interested in scientific work dealing with investigations of biologically active compounds as potential drugs. The authors of this manuscript were plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting

Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors

We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry

The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited

CO2—A Crisis or Novel Functionalization Opportunity?

The growing emission of carbon dioxide (CO2), combined with its ecotoxicity, is the reason for the intensification of research on the new technology of CO2 management. Currently, it is believed that it is not possible to eliminate whole CO2 emissions. However, a sustainable balance sheet is possible. The solution is technologies that use carbon dioxide as a raw material. Many of these methods are based on CO2 methanation, for example, projects such as Power-to-Gas, production of fuels, or polymers. This article presents the concept of using CO2 as a raw material, the catalytic conversion of carbon dioxide to methane, and consideration on CO2 methanation catalysts and their design

Between Descriptors and Properties: Understanding the Ligand Efficiency Trends for G Protein-Coupled Receptor and Kinase Structure–Activity Data Sets

The chemical meaning of the ligand efficiency (LE) metrics is explained in this paper using a large G protein-coupled receptor (GPCR) and kinase structure–activity (IC₅₀, <i>K</i>_i) data set. Although there is a controversy in the literature regarding both the mathematical validity and the performance of LE, it is in common use as an early estimator for drug optimization. Apparently, the numerous con arguments are not convincing enough. We show here for the first time that the main misunderstanding of the chemical meaning of LE is its interpretation as a molecular descriptor connected with a single molecule. Instead, LE should be interpreted as a statistical property. We show that the LE, which is designed as a regression of a binding property on the heavy atom count (HAC), is correlated to the reciprocal of the molecular weight because of Avogadro statistics. This indicates that the hyperbolic model of LE is basically a consequence of a nonbinding effect, an increase in the number of ligands that are available to a receptor for smaller molecules, and not a real increase in the binding potency for a single HAC as interpreted in the literature. Accordingly, we need to revisit and carefully reevaluate LE-based molecular comparisons

A Thiosemicarbazone Derivative as a Booster in Photodynamic Therapy—A Way to Improve the Therapeutic Effect

Photodynamic therapy is one of the most patient friendly and promising anticancer therapies. The active ingredient is irradiated protoporphyrin IX, which is produced in the body that transfers energy to the oxygen-triggering phototoxic reaction. This effect could be enhanced by using iron chelators, which inhibit the final step of heme biosynthesis, thereby increasing the protoporphyrin IX concentration. In the presented work, we studied thiosemicarbazone derivative, which is a universal enhancer of the phototoxic effect. We examined several genes that are involved in the transport of the heme substrates and heme itself. The results indicate that despite an elevated level of ABCG2, which is responsible for the PpIX efflux, its concentration in a cell is sufficient to trigger a photodynamic reaction. This effect was not observed for 5-ALA alone. The analyzed cell lines differed in the scale of the effect and a correlation with the PpIX accumulation was observed. Additionally, an increased activation of the iron transporter MFNR1 was also detected, which indicated that the regulation of iron transport is essential in PDT