Theoretical simulations elucidate the role of naphthalenic species during methanol conversion within H-SAPO-34

The role of naphthalenic species during the methanol-to-olefins (MTO) process in a silicoaluminophosphate zeolitic material exhibiting the chabazite topology (H-SAPO-34) has been studied from first principles. These species could either act as active olefin-eliminating compounds or as precursors for deactivating species. Results incorporating van der Waals contributions for finite large clusters point out that successive methylation steps of naphthalenic compounds are feasible. The calculated intrinsic activation barrier is relatively independent of the number of methyl groups already attached on the aromatic compound and is approximately 140 kJ mol(-1). The influence of the composition of the catalyst and hence the acidic strength on the intrinsic chemical kinetics was investigated in detail through comparison with the isostructural high-silicon material. Apparent chemical kinetics, starting from adsorbed methanol on the acid site, were also computed. The initiation steps of the side-chain route starting from a trimethylated naphthalenium ion were also examined. The actual side-chain methylation exhibits a high barrier and hence this mechanism involving methylated naphthalenes is not expected to be an active ethene-eliminating route in H-SAPO-34.     

Novel hierarchical classification and visualization method for multiobjective optimization of drug properties: application to structure-activity relationship analysis of cytochrome P450 metabolism

In the lead optimization process, medicinal chemists must consider various chemical properties of active compounds, including ADME/Tox properties, and find the best compromise among these. This study presents a novel data mining method for multiobjective optimization of chemical properties, which consists of the hierarchical classification and visualization of multidimensional data. A hierarchical classification tree model is generated by an extension of recursive partitioning that utilizes averaged information gains for multiple objective variables as a quality-of-split criterion. All the hierarchically structured data objects are represented using a large-scale data visualization technique. The technique is an extension of HeiankyoView, which displays data objects as colored icons and group nodes as rectangular borders. Each icon is divided into subregions with different colors, so that it can present multidimensional data according to brightness of the colors. The proposed method was applied to the structure-activity relationship analysis for cytochrome P450 (CYP) substrates. The substrate specificity of six CYP isoforms was successfully delineated: e.g., CYP2C9 substrates are anionic compounds, while CYP2D6 substrates are cationic; and CYP2E1 substrates are smaller compounds, while CYP3A4 substrates are larger compounds.     

Cluster models and ab initio calculations of (19)F NMR isotropic chemical shifts for inorganic fluorides

(19)F NMR isotropic chemical shift (delta(iso)) calculations are performed in crystallized compounds using the GIAO method with the B3LYP hybrid functional at DFT level. Clusters centered on the studied fluorine atoms mimic the crystalline structures. The 6-311+G(d) basis set is chosen for the central fluorine atom, and the LanL2DZ basis set for the others. The metal atoms are described by the 3-21G(2d) basis set or, when not available, by the CRENBL basis set with the corresponding ECP, and augmented with 2d polarization functions when existing. First, for high-symmetry systems (MF, MF(2), and MF(3) compounds), a systematization of the cluster building up from coordination spheres is proposed, generalized to fluoroperovskites and fluoroaluminates KAlF(4) and RbAlF(4). When applied to rather low symmetry systems such as barium fluorometalates BaMgF(4), BaZnF(4), and Ba(2)ZnF(6), the definition of the coordination spheres is far from easy. Then, for structures built up from a MF(6) octahedron network, we may define different "starting clusters": [FM(2)F(8)] for the shared fluorine atoms, [FMF(4)] for the unshared ones, and [FBa(4)](7+) for the "free" ones. Analogous "starting clusters" are then tested on compounds from the NaF-AlF(3), BaF(2)-AlF(3), and CaF(2)-AlF(3) binary systems and for alpha-BaCaAlF(7) that are also built up from a MF(6) octahedron network. For each of these corresponding fluorine sites, delta(iso) values are calculated with the "starting clusters" and several larger clusters and compared to the experimental delta(iso) values. For the barium-containing clusters, the RMS deviation is equal to 51 ppm. It is suggested that this result may be related to the poor quality of the barium basis sets for which no polarization functions are available for the moment. In total, chemical shifts were calculated for 122 fluorine sites, in a various range of compounds. For the clusters without barium, the ab initio method leads to a RMS equal to 22 ppm, which is a quite nice result keeping in mind that the (19)F chemical shift range is larger than 200 ppm.     

Aromaticity and antiaromaticity in transition-metal systems

Aromaticity is an important concept in chemistry primarily for organic compounds, but it has been extended to compounds containing transition-metal atoms. Recent findings of aromaticity and antiaromaticity in all-metal clusters have stimulated further research in describing the chemical bonding, structures and stability in transition-metal clusters and compounds on the basis of aromaticity and antiaromaticity, which are reviewed here. The presence of d-orbitals endows much more diverse chemistry, structure and chemical bonding to transition-metal clusters and compounds. One interesting feature is the existence of a new type of aromaticity-delta-aromaticity, in addition to sigma- and pi-aromaticity which are the only possible types for main-group compounds. Another striking characteristic in the chemical bonding of transition-metal systems is the multi-fold nature of aromaticity, antiaromaticity or even conflicting aromaticity. Separate sets of counting rules have been proposed for cyclic transition-metal systems to account for the three types of sigma-, pi- and delta-aromaticity/antiaromaticity. The diverse transition-metal clusters and compounds reviewed here indicate that multiple aromaticity and antiaromaticity may be much more common in chemistry than one would anticipate. It is hoped that the current review will stimulate interest in further understanding the structure and bonding, on the basis of aromaticity and antiaromaticity, of other known or unknown transition-metal systems, such as the active sites of enzymes or other biomolecules which contain transition-metal atoms and clusters.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

Synthesis and sol–gel assembly of nanophosphors

Some recent works made in our group on inorganic nanophosphors are briefly reviewed in this paper. We first present the synthesis of highly concentrated semiconductor quantum dot colloids allowing the extension of the well-known oxide sol–gel process to chalcogenide compounds. Secondly, we show the synthesis and the chemical functionalization of lanthanide-doped insulator nanoparticles. In particular, the annealing process of these particles at high temperature leads to highly bright nanocrystals, which can be used as biological luminescent labels or for integration in transparent luminescent coatings. Finally, we consider luminescent transition metal clusters, which combine the inorganic structure of nanoparticles with the monodispersity and the easy functionalization of the organic molecules. Emphasis is put on the original thermochromic luminescence properties of copper iodide clusters trapped in siloxane-based films.     

Nanostructured metal clusters in polymeric field as a model of artificial enzyme

Nanostructured metal clusters are often stabilized by surrounding with synthetic polymers. The most popular application of metal clusters is as catalysts for various chemical reactions. The catalysis of metal clusters can be compared to the action of enzyme. Thus, the surrounding polymer of metal clusters can be assigned as a polymeric field and has the functions similar to the protein surrounding the active site of enzyme. Interaction between surrounding polymers and reactive substrates can affect the activity and selectivity of the metal cluster catalysts. Polymers play an important role not only in the catalytic process but also in the formation process of metal clusters. Polymers not only stabilize the metal clusters at a nonequilibrium state, but also control the nanostructure of metal clusters, especially the core/shell structure of bimetallic clusters by coordination interaction.     

Dynamic Expression of DNA Complexation with Self‐assembled Biomolecular Clusters

We report herein the implementation of a dynamic covalent chemistry approach to the generation of multivalent clusters for DNA recognition. We show that biomolecular clusters can be expressed in situ by a programmed self‐assembly process using chemoselective ligations. The cationic clusters are shown, by fluorescence displacement assay, gel electrophoresis and isothermal titration calorimetry, to effectively complex DNA through multivalent interactions. The reversibility of the ligation was exploited to demonstrate that template effects occur, whereby DNA imposes component selection in order to favor the most active DNA‐binding clusters. Furthermore, we show that a chemical effector can be used to trigger DNA release through component exchange reactions.     

Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships

From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.     

SAR monitoring of evolving compound data sets using activity landscapes

In pharmaceutical research, collections of active compounds directed against specific therapeutic targets usually evolve over time. Small molecule discovery is an iterative process. New compounds are discovered, alternative compound series explored, some series discontinued, and others prioritized. The design of new compounds usually takes into consideration prior chemical and structure-activity relationship (SAR) knowledge. Hence, historically grown compound collections represent a viable source of chemical and SAR information that might be utilized to retrospectively analyze roadblocks in compound optimization and further guide discovery projects. However, SAR analysis of large and heterogeneous sets of active compounds is also principally complicated. We have subjected evolving compound data sets to SAR monitoring using activity landscape models in order to evaluate how composition and SAR characteristics might change over time. Chemotype and potency distributions in evolving data sets directed against different therapeutic targets were analyzed and alternative activity landscape representations generated at different points in time to monitor the progression of global and local SAR features. Our results show that the evolving data sets studied here have predominantly grown around seed clusters of active compounds that often emerged early on, while other SAR islands remained largely unexplored. Moreover, increasing scaffold diversity in evolving data sets did not necessarily yield new SAR patterns, indicating a rather significant influence of "me-too-ism" (i.e., introducing new chemotypes that are similar to already known ones) on the composition and SAR information content of the data sets.     

On the simultaneous optimization of pressure and layout for gas permeation membrane systems

We present a methodology for the simultaneous optimization of pressure and network configurations for gas separation membrane permeators. The methodology targets and refines pressure clusters for efficient operation of membrane networks and follows a three-stage strategy. The first stage produces a pressure target curve (PTC) that allows the identification of Pareto optimal pressure cluster combinations. This is followed by a second stage, where the different optimal pressure ratios are used in an optimal search for process structures to identify the performance of the individual clusters. The third stage processes the information generated in the first two stages in a generalized process superstructure model. Throughout the methodology, a modified process synthesis model for membrane network optimization and design is employed which can be optimized robustly using the simulated annealing algorithm. Three illustrative examples are presented to demonstrate the proposed methodology for simultaneous pressure and layout optimization.     

Metalloid Al- and Ga-clusters: a novel dimension in organometallic chemistry linking the molecular and the solid-state areas?

Formation and fragmentation of metal-metal bonds on the way between stable metal compounds in which the metal atoms are oxidised (e.g. isolated species in solution or metal salts in bulk) and the bulk metal are the fundamental steps to understand this process in which formation and chemical behaviour of metalloid Al and Ga clusters as intermediates are essential. Many examples of metalloid Al and Ga clusters show that their formation reflects a high degree of complexity like that of the simple seeming formation of the bulk metal itself: starting from metastable Al(i) and Ga(i) solutions containing small molecular entities, metalloid clusters grow during many self-organization steps including aggregation as well as irreversible redox cascades. This novel class of clusters seems to open a new dimension in chemistry between the molecular and the solid-state area, because, for the first time, it is shown that under well selected conditions definite molecular species, i.e. metalloid clusters, grow via the formation of additional metal-metal bonds and that the solid metal represents the final step.     

Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach

We simulate the coherent stage of Cu precipitation in α-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migration energy as a function of the local chemical environment is provided on-the-fly by a neural network, trained with high precision on values calculated with the nudged elastic band method, using a suitable interatomic potential. To speed up the simulation, however, we modify the standard AKMC algorithm by treating large Cu clusters as objects, similarly to object kinetic Monte Carlo approaches. Seamless matching between the fully atomistic and the coarse-grained approach is achieved again by using a neural network, that provides all stability and mobility parameters for large Cu clusters, after training on atomistically informed results. The resulting hybrid algorithm allows long thermal annealing experiments to be simulated, within a reasonable CPU time. The results obtained are in very good agreement with several series of experimental data available from the literature, spanning over different conditions of temperature and alloy composition. We deduce from these results and relevant parametric studies that the mobility of Cu clusters containing one vacancy plays a central role in the precipitation mechanism.     

Mass spectrometry-based strategies for screening of bioactive natural products

Natural products (NPs) are combinatorial chemical libraries with diversities in chemical structures and pharmacological activities. Screening active compounds is in many cases an important factor in drug discovery. It was not easy to screen out the bioactive compounds from complex extracts consisting of many NPs. Development of rapid, effective and accurate methods is in high demand. During last decades, mass spectrometry (MS)-based strategies, combining isolation, structures, and bioactivity in a single run, were programmed in the NPs screening. The current article reviews different assay formats and applications of MS-based methods for screening of active NPs. This review is divided into three sections based on methods classification. The first part introduces binding-based screening methods that directly assess the binding characteristics of a candidate molecule to its target. The second part describes function-based screening methods that monitor the functional output of a target-dependent biochemical reaction. The third part briefly discusses serum pharmacochemistry-based screening methods that analyze absorbed components and metabolites in plasma after oral administration of NPs extracts.