Spatially Formed Tenacious Nickel-Supported Bimetallic Catalysts for CO2 Methanation under Conventional and Induction Heating.

The paper introduces spatially stable Ni-supported bimetallic catalysts for CO2 methanation. The catalysts are a combination of sintered nickel mesh or wool fibers and nanometal particles, such as Au, Pd, Re, or Ru. The preparation involves the nickel wool or mesh forming and sintering into a stable shape and then impregnating them with metal nanoparticles generated by a silica matrix digestion method. This procedure can be scaled up for commercial use. The catalyst candidates were analyzed using SEM, XRD, and EDXRF and tested in a fixed-bed flow reactor. The best results were obtained with the Ru/Ni-wool combination, which yields nearly 100% conversion at 248 °C, with the onset of reaction at 186 °C. When we tested this catalyst under inductive heating, the highest conversion was observed already at 194 °C.

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.

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.

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem.

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.

Novel Benzene-Based Carbamates for AChE/BChE Inhibition: Synthesis and Ligand/Structure-Oriented SAR Study.

A series of new benzene-based derivatives was designed, synthesized and comprehensively characterized. All of the tested compounds were evaluated for their in vitro ability to potentially inhibit the acetyl- and butyrylcholinesterase enzymes. The selectivity index of individual molecules to cholinesterases was also determined. Generally, the inhibitory potency was stronger against butyryl- compared to acetylcholinesterase; however, some of the compounds showed a promising inhibition of both enzymes. In fact, two compounds (23, benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate and 28, benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl) (methyl)carbamate) had a very high selectivity index, while the second one (28) reached the lowest inhibitory concentration IC50 value, which corresponds quite well with galanthamine. Moreover, comparative receptor-independent and receptor-dependent structure⁻activity studies were conducted to explain the observed variations in inhibiting the potential of the investigated carbamate series. The principal objective of the ligand-based study was to comparatively analyze the molecular surface to gain insight into the electronic and/or steric factors that govern the ability to inhibit enzyme activities. The spatial distribution of potentially important steric and electrostatic factors was determined using the probability-guided pharmacophore mapping procedure, which is based on the iterative variable elimination method. Additionally, planar and spatial maps of the host⁻target interactions were created for all of the active compounds and compared with the drug molecules using the docking methodology.

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, Ki) 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.

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.

The analysis of the market success of FDA approvals by probing top 100 bestselling drugs.

Target-oriented drug discovery is the main research paradigm of contemporary drug discovery. In target-oriented approaches, we attempt to maximize in vitro drug potency by finding the optimal fit to the target. This can result in a higher molecular complexity, in particular, the higher molecular weight (MW) of the drugs. However, a comparison of the successful developments of pharmaceuticals with the general trends that can be observed in medicinal chemistry resulted in the conclusion that the so-called molecular obesity is an important reason for the attrition rate of drugs. When analyzing the list of top 100 drug bestsellers versus all of the FDA approvals, we discovered that on average lower-complexity (MW, ADMET score) drugs are winners of the top 100 list in terms of numbers but that, especially, up to some optimal MW value, a higher molecular complexity can pay off with higher incomes. This indicates that slim drugs are doing better but that fat drugs are bigger fishes to catch.

The Eighth Central European Conference "Chemistry towards Biology": Snapshot.

The Eighth Central European Conference "Chemistry towards Biology" was held in Brno, Czech Republic, on August 28-September 1, 2016 to bring together experts in biology, chemistry and design of bioactive compounds; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topics of the conference covered "Chemistry towards Biology", meaning that the event welcomed chemists working on biology-related problems, biologists using chemical methods, and students and other researchers of the respective areas that fall within the common scope of chemistry and biology. The authors of this manuscript are 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.

Ligand-Based Virtual Screening in a Search for Novel Anti-HIV-1 Chemotypes.

In a search for new anti-HIV-1 chemotypes, we developed a multistep ligand-based virtual screening (VS) protocol combining machine learning (ML) methods with the privileged structures (PS) concept. In its learning step, the VS protocol was based on HIV integrase (IN) inhibitors fetched from the ChEMBL database. The performances of various ML methods and PS weighting scheme were evaluated and applied as VS filtering criteria. Finally, a database of 1.5 million commercially available compounds was virtually screened using a multistep ligand-based cascade, and 13 selected unique structures were tested by measuring the inhibition of HIV replication in infected cells. This approach resulted in the discovery of two novel chemotypes with moderate antiretroviral activity, that, together with their topological diversity, make them good candidates as lead structures for future optimization.