Outstanding Performance of Chalcopyrite (CuFeS 2 ) for Gaseous Elemental Mercury Adsorption: Mechanism, Kinetics, and Structure–Activity Relationship

This report summarizes the results of a study carried out on six pulverized coal-fired power plants in western Canada burning subbituminous coal for the mass-balance and speciation of mercury. The main objectives of this study were to: determine the total gaseous mercury (TGM) emitted from stacks of power plants using the Ontario Hydro method; identify the speciation of emitted mercury such as metallic (Hg(0)) and gaseous elemental (GEM) mercury; and perform mass-balance calculations of mercury for milled-coal, bottom ash, electrostatic precipitators (ESP) fly ash and stack-emitted mercury based on three tests. Sampling of mercury was carried out using the Ontario Hydro method and mercury was determined using the USEPA method 7473 by cold vapor atomic absorption (CVAAS). The sample collection efficiencies confirmed that both oxidized and the elemental mercury had been successfully sampled at all power plants. The total gaseous mercury emitted (TGM) is 6.95-15.66 g h(-1) and is mostly in gaseous elemental mercury (GEM, Hg(0)) form. The gaseous elemental mercury is emitted at a rate of 6.59-12.62 g h(-1). Reactive gaseous mercury (RGM, Hg(2+)) is emitted at a rate of 0.34-3.68 g h(-1). The rate of emission of particulate mercury (Hg(p)) is low and is in the range 0.005-0.076 g h(-1). The range of mass-balances for each power plant is more similar to the variability in measured mercury emissions, than to the coal and ash analyses or process data. The mass-balance calculations for the six power plants, performed on results of the three tests at each power plant, are between 86% and 123%, which is acceptable and within the range 70-130%. The variation in mass-balance of mercury for the six power plants is mostly related to the variability of coal feed rate.

A new and general approach to forming Structure Activity Relationships (SARs) is described. This is based on representing chemical structure by atoms and their bond connectivities in combination with the Inductive Logic Programming (ILP) algorithm Progol. Existing SAR methods describe chemical structure using attributes which are general properties of an object. It is not possible to map directly chemical structure to attribute-based descriptions, as such descriptions have no internal organisation. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SAR of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two sub-sets, 188 compounds that were amenable to regression, and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network generated SARs. The Progol SAR has the advantages that it did not need the use of any indicator variables hand-crafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, Progol formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on a new automatically generated structural alert for mutagenicity.

Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists

Structure-activity relationships (SARs) can display very different features. Small chemical modifications of active molecules often dramatically alter biological responses. By contrast, structurally diverse molecules can have similar activity. SARs can also be heterogeneous in nature. For example, for structurally diverse molecules with similar activity, closely related analogs might have significant differences in potency. Given the inherent complexity of SARs, it has been very difficult to estimate SAR characteristics from molecular structure. On the basis of systematic correlation of 2D structural similarity and compound potency, we have developed a function termed "SAR Index" that quantitatively describes the nature of SARs and establishes different SAR categories: continuous, discontinuous, heterogeneous-relaxed, and heterogeneous-constrained. These heterogeneous SAR categories are described for the first time. Given a set of active compounds and their potency values, SAR Index calculations can estimate how likely it is to identify structurally distinct molecules having similar activity.

Activity cliffs are formed by structurally similar compounds with significant differences in potency and represent an extreme form of structure-activity relationships discontinuity. By contrast, regions of structure-activity relationships continuity in compound data sets result from the presence of structurally increasingly diverse compounds retaining similar activity. Previous studies have revealed that structure-activity relationships information extracted from large compound data sets is often heterogeneous in nature containing both continuous and discontinuous structure-activity relationships components. Structure-activity relationships discontinuity and continuity are often represented by different compound series, independent of each other. Here, we have searched different compound data sets for the presence of structure-activity relationships continuity within the vicinity of prominent activity cliffs. For this purpose, we have designed and implemented a computational approach utilizing particle swarm optimization to examine the structural neighborhood of activity cliffs for continuous structure-activity relationships components. Structure-activity relationships continuity in the structural neighborhood of activity cliffs was relatively rarely observed. However, in a number of cases, notable structure-activity relationships continuity was detected in the vicinity of prominent activity cliffs. Exemplary local structure-activity relationships environments displaying these characteristics were analyzed in detail. Thus, the structure-activity relationships environment of activity cliffs must not necessarily be discontinuous in nature, and local structure-activity relationships continuity and discontinuity can occur in a concerted manner in series of structurally related compounds.

Palladium and platinum 3,5-diacetyl-1,2,4-triazol bis(thiosemicarbazones): Chemistry, cytotoxic activity and structure–activity relationships

The exploration of structure-activity relationships (SARs) of small molecules is a central aspect of medicinal chemistry. Typically, SARs are analyzed on a one-by-one basis, and chemical intuition and experience play an important role in this process. Since the 1960s, computational approaches have been developed to aid in SAR exploration that largely, but not exclusively, rely on the quantitative (Q)SAR paradigm. Accordingly, QSAR analysis has long been a mainstay of compound optimization efforts. However, the strong compound class dependence of SAR features and their intrinsic heterogeneity often pose severe constraints on the applicability of these methods. In addition to QSAR approaches, conceptually different molecular similarity methods are also applied to identify novel active compounds. In order to complement and further extend the current repertoire of computational methods, SAR analysis functions have recently been introduced that evaluate and compare SAR features on a large scale, extract SAR information from compound data sets and prioritize SARs that are promising targets for optimization. SAR analysis functions are designed to systematically profile and compare SARs contained in different data sets and characterize both global and local SAR features. Numerical SAR analysis is complemented by intuitive graphical representations of SAR landscapes.

The antioxidant activities of 24 isoflavonoids that were previously isolated as pure compounds from Dalbergia parviflora were evaluated using three different in vitro antioxidant-based assay systems: xanthine/xanthine oxidase (X/XO), ORAC, and DPPH. The isolates consisted of three subgroups, namely isoflavones, isoflavanones, and isoflavans, each of which appeared to have diversified substituents, and were thus ideal for the study of their structure-activity relationships (SARs). The SAR analysis was performed using the results obtained from both the inter-subgroup isoflavonoids with the same substitution pattern and the intra-subgroup compounds with different substitution patterns. The inter-subgroup comparison showed that the isoflavones exhibited the highest antioxidant activities based on all three assays. The intra-subgroup analysis showed that the additional presence of an OH group in Ring B at either R3′ or R5′ from the basic common structure of the R7-OH of Ring A and the R4′-OH (or -OMe) of Ring B greatly increased the antioxidant activities of all of the isoflavonoid subgroups and that other positions of OH and OMe substitutions exerted different effects on the activities depending on the subgroup and assay type. Therefore, based on the structural diversity of the isoflavonoids in D. parviflora, the present study provides the first clarification of the detailed antioxidant SARs of isoflavonoids.

Discontinuity in structure-activity relationships (SARs) is caused by so-called activity cliffs and represents one of the major caveats in SAR modeling and lead optimization. At activity cliffs, small structural modifications of compounds lead to substantial differences in potency that are essentially unpredictable using quantitative structure-activity relationship (QSAR) methods. In order to better understand SAR discontinuity at the molecular level of detail, we have analyzed different compound series in combinatorial analog graphs and determined substitution patterns that introduce activity cliffs of varying magnitude. So identified SAR determinants were then analyzed on the basis of complex crystal structures to enable a structural interpretation of SAR discontinuity and underlying activity cliffs. In some instances, SAR discontinuity detected within analog series could be well rationalized on the basis of structural data, whereas in others a structural explanation was not possible. This reflects the intrinsic complexity of small molecule SARs and suggests that the analysis of short-range receptor-ligand interactions seen in X-ray structures is insufficient to comprehensively account for SAR discontinuity. However, in other cases, SAR information extracted from ligands was incomplete but could be deduced taking X-ray data into account. Thus, taken together, these findings illustrate the complementarity of ligand-based SAR analysis and structural information.

Citation: 'structure–activity relationship' in the IUPAC Compendium of Chemical Terminology, 3rd ed.; International Union of Pure and Applied Chemistry; 2006. Online version 3.0.1, 2019. 10.1351/goldbook.ST06845 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

The analysis of structure-activity relationships (SARs) becomes rather challenging when large and heterogeneous compound data sets are studied. In such cases, many different compounds and their activities need to be compared, which quickly goes beyond the capacity of subjective assessments. For a comprehensive large-scale exploration of SARs, computational analysis and visualization methods are required. Herein, we introduce a two-layered SAR visualization scheme specifically designed for increasingly large compound data sets. The approach combines a new compound pair-based variant of generative topographic mapping (GTM), a machine learning approach for nonlinear mapping, with chemical space networks (CSNs). The GTM component provides a global view of the activity landscapes of large compound data sets, in which informative local SAR environments are identified, augmented by a numerical SAR scoring scheme. Prioritized local SAR regions are then projected into CSNs that resolve these regions at the level of individual compounds and their relationships. Analysis of CSNs makes it possible to distinguish between regions having different SAR characteristics and select compound subsets that are rich in SAR information.

Structure activity relationship (SAR) of fibroblast activation protein alpha (FAP) inhibitors will be useful to evaluate bioactivities of candidates. To discuss SAR of FAP inhibitors, two alignment styles were carried out to build QSAR models of FAP inhibitors. HQSAR was used to construct 2D-QSAR after the selection of training set and test set by principal component analysis method. Meanwhile, 3D-QSAR models were constructed by comparative molecular field analysis and comparative molecular similarity indices analysis method and optimized by FOCUS method. All the QSAR models were validated by cross-validation and test set, and the targeted QSAR model was selected by comprehensive evaluation containing cross-validation coefficient, correlation coefficient and consistency with docking studies. The result suggests that 2D-QSAR model may be insufficient to evaluate SAR of FAP inhibitors, while 3D-QSAR model with S+H+D_F functional fields could be applied to characterize the SAR based on docking conformation alignment.

Activity landscapes are defined by potency and similarity distributions of active compounds and reflect the nature of structure-activity relationships (SARs). Three-dimensional (3D) activity landscapes are reminiscent of topographical maps and particularly intuitive representations of compound similarity and potency distributions. From their topologies, SAR characteristics can be deduced. Accordingly, idealized theoretical landscape models have been utilized to rationalize SAR features, but "true" 3D activity landscapes have not yet been described in detail. Herein we present a computational approach to derive approximate 3D activity landscapes for actual compound data sets and to analyze exemplary landscape representations. These activity landscapes are generated within a consistent reference frame so that they can be compared across different activity classes. We show that SAR features of compound data sets can be derived from the topology of landscape models. A notable correlation is observed between global SAR phenotypes, assigned on the basis of SAR discontinuity scoring, and characteristic landscape topologies. We also show that different molecular representations can substantially alter the topology of activity landscapes for a given data set and modulate the formation of activity cliffs, which represent the most prominent landscape features. Depending on the choice of molecular representations, compounds forming a steep activity cliff in a given landscape might be separated in another and no longer form a cliff. However, comparison of alternative activity landscapes makes it possible to focus on compound subsets having high SAR information content.

The study of structure-activity relationships (SARs) of small molecules is of fundamental importance in medicinal chemistry and drug design. Here, we introduce an approach that combines the analysis of similarity-based molecular networks and SAR index distributions to identify multiple SAR components present within sets of active compounds. Different compound classes produce molecular networks of distinct topology. Subsets of compounds related by different local SARs are often organized in small communities in networks annotated with potency information. Many local SAR communities are not isolated but connected by chemical bridges, i.e., similar molecules occurring in different local SAR contexts. The analysis makes it possible to relate local and global SAR features to each other and identify key compounds that are major determinants of SAR characteristics. In many instances, such compounds represent start and end points of chemical optimization pathways and aid in the selection of other candidates from their communities.

A computational molecular network analysis of various high-throughput screening (HTS) data sets including inhibition assays and cell-based screens organizes screening hits according to different local structure-activity relationships (SARs). The resulting network representations make it possible to focus on different local SAR environments in screening data. We have designed a simple scoring function accounting for similarity and potency relationships among hits that identifies SAR pathways leading from active compounds in different SAR contexts to key compounds forming activity cliffs. From these pathways, SAR information can be extracted and utilized to select hits for further analysis. In clusters of hits related by different local SARs, alternative pathways can be systematically explored and ranked according to SAR information content, which makes it possible to prioritize hits in a consistent manner.