Elucidating the Reaction Pathways of Veratrylglycero-ß-Guaiacyl Ether Degradation over Metal-Free Solid Acid Catalyst with Hydrogen.

Efficient cleavage of ß-O-4 bonds in lignin to high-yield aromatic compounds for the potential production of fuels and chemicals is vital for the economics of the modern biorefinery industry. This work is distinct in that a detailed mechanistic analysis of the reaction pathways of veratrylglycero-ß-guaiacyl ether (VGE) catalyzed by transition-metal-free solid acid zeolite in aqueous conditions at high hydrogen pressure has been performed. VGE degradation produced high monomers yields (≈87 %), including guaiacol (48.2 %), 1-(3,4-dimethoxyphenyl)ethanol (10.3 %), 1-(3,4-dimethoxyphenyl)-2-propanol (6.1 %), 3,4-dimethoxyphenylpropanol (4.7 %), 3,4-dimethoxycinnamyl alcohol (4.1 %), and 1,2-dimethoxy-4-propylbenzene (2 %). The products were identified and confirmed by the in situ solid-state magic angle spinning (MAS) 13 C NMR spectroscopy in real-time conditions and the two-dimensional gas chromatography (GC×GC). A variety of products reveal the crucial role of hydrogen, water, and acid sites for heterolytic cleavage of the ß-O-4 bond in VGE. Decarbonylation, hydrogenolysis, hydrogenation, and dehydration reaction pathways are proposed and further validated using first-principles calculations.

Thermal-wave resonant cavity signal processing.

The thermal-wave resonant cavity (TWRC) technique has been used for thermal diffusivity measurements by many researchers. This study aims to reduce the uncertainty associated with TWRC signal processing (curve fitting) by means of numerical simulation and experimental verification. Simulations show that the plot of signal amplitude versus cavity length can be fitted to a simplified model reported previously when the initial fitting position is at least twice the thermal-wave diffusion length (2 µg), and that the uncertainty caused by different end positions is negligible in the range of 6-10 µg. Upon consideration of the simulation results, signal-to-noise ratio, and clearly defined amplitude curve shape, fitting ranges of about 2.2-8.0 µg and 2.2-8.7 µg were chosen for the experimental data. Thermal diffusivity values (1.438 ± 0.001) × 10-7 and (1.436 ± 0.001) × 10-7 m2 s-1, respectively, were obtained for distilled water, in excellent agreement with the accepted literature value. The ratio of standard deviation to the mean value is smaller than 0.07%, one order of magnitude lower than typical results reported in the literature. Similar simulation results were obtained for air and methanol as intra-cavity samples.

Comparative molecular surface analysis (CoMSA) for virtual combinatorial library screening of styrylquinoline HIV-1 blocking agents.

We used comparative molecular surface analysis to design molecules for the synthesis as part of the search for new HIV-1 integrase inhibitors. We analyzed the virtual combinatorial library (VCL) constituted from various moieties of styrylquinoline and styrylquinazoline inhibitors. Since imines can be applied in a strategy of dynamic combinatorial chemistry (DCC), we also tested similar compounds in which the -C=N- or -N=C- linker connected the heteroaromatic and aromatic moieties. We then used principal component analysis (PCA) or self-organizing maps (SOM), namely, the Kohonen neural networks to obtain a clustering plot analyzing the diversity of the VCL formed. Previously synthesized compounds of known activity, used as molecular probes, were projected onto this plot, which provided a set of promising virtual drugs. Moreover, we further modified the above mentioned VCL to include the single bond linker -C-N- or -N-C-. This allowed increasing compound stability but expanded also the diversity between the available molecular probes and virtual targets. The application of the CoMSA with SOM indicated important differences between such compounds and active molecular probes. We synthesized such compounds to verify the computational predictions.

Application of a quantitative structure retention relationship approach for the prediction of the two-dimensional gas chromatography retention times of polycyclic aromatic sulfur heterocycle compounds.

Information on the sulfur classes present in petroleum is a key factor in determining the value of refined products and processing behavior in the refinery. A large part of the sulfur present is included in polycyclic aromatic sulfur heterocycles (PASHs), which in turn are difficult to desulfurize. Furthermore, some PASHs are potentially more mutagenic and carcinogenic than polycyclic aromatic hydrocarbons, PAHs. All of this calls for improved methods for the identification and quantification of individual sulfur species. Recent advances in analytical techniques such as comprehensive two-dimensional gas chromatography (GC×GC) have enabled the identification of many individual sulfur species. However, full identification of individual components, particularly in virgin oil fractions, is still out of reach as standards for numerous compounds are unavailable. In this work, a method for accurately predicting retention times in GC×GC using a QSRR (quantitative structure retention relationship) method was very helpful for the identification of individual sulfur compounds. Retention times for 89 saturated, aromatic, and polyaromatic sulfur-containing heterocyclic compounds were determined using two-dimensional gas chromatography. These retention data were correlated with molecular descriptors generated with CODESSA software. Two independent QSRR relationships were derived for the primary as well as the secondary retention characteristics. The predictive ability of the relationships was tested by using both independent sets of compounds and a cross-validation technique. When the corresponding chemical standards are unavailable, the equations developed for predicting retention times can be used to identify unknown chromatographic peaks by matching their retention times with those of sulfur compounds of known molecular structure.

Diesel Surrogate Fuels for Engine Testing and Chemical-Kinetic Modeling: Compositions and Properties.

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the four surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. This work documents the surrogate-fuel creation process and the results of the property measurements.

Use of the Kohonen neural network for rapid screening of ex vivo anti-HIV activity of styrylquinolines.

Using the Kohonen neural network, the electrostatic potentials on the molecular surfaces of 14 styrylquinoline derivatives were drawn as comparative two-dimensional maps and compared with their known human immunodeficiency virus (HIV)-1 replication blocking potency in cells. A feature of the potential map was discovered to be related with the HIV-1 blocking activity and was used to unmask the activity of further five analogues, previously described but whose cytotoxicity precluded an estimation of their activity, and to predict the activity of 10 new compounds while the experimental data were unknown. The measurements performed later turned out to agree with the predictions.

Probability issues in molecular design: predictive and modeling ability in 3D-QSAR schemes.

In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.

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.

A comparative molecular surface analysis (COMSA). A new efficient technique for drug design.

Several applications of the COMSA were discussed including a series of the potential anti-HIV drugs and a series of dyes.

Diaromatic sulphur-containing 'naphthenic' acids in process waters.

Polar organic compounds found in industrial process waters, particularly those originating from biodegraded petroleum residues, include 'naphthenic acids' (NA). Some NA have been shown to have acute toxicity to fish and also to produce sub-lethal effects. Whilst some of these toxic effects are produced by identifiable carboxylic acids, acids such as sulphur-containing acids, which have been detected, but not yet identified, may produce others. Therefore, in the present study, the sulphur-containing acids in oil sands process water were studied. A fraction (ca 12% by weight of the total NA containing ca 1.5% weight sulphur) was obtained by elution of methylated NA through an argentation solid phase extraction column with diethyl ether. This was examined by multidimensional comprehensive gas chromatography-mass spectrometry (GCxGC-MS) in both nominal and high resolution mass accuracy modes and by GCxGC-sulphur chemiluminescence detection (GCxGC-SCD). Interpretation of the mass spectra and retention behaviour of methyl esters of several synthesised sulphur acids and the unknowns allowed delimitation of the structures, but not complete identification. Diaromatic sulphur-containing alkanoic acids were suggested. Computer modelling of the toxicities of some of the possible acids suggested they would have similar toxicities to one another and to dehydroabietic acid. However, the sulphur-rich fraction was not toxic or estrogenic to trout hepatocytes, suggesting the concentrations of sulphur acids in this sample were too low to produce any such effects in vitro. Further samples should probably be examined for these compounds.

Modeling robust QSAR. 2. iterative variable elimination schemes for CoMSA: application for modeling benzoic acid pKa values.

A variety of issues decide the efficiency of 3D QSAR methods, and their practical importance for drug design is still controversial. This refers both to the predictive ability and the possibility for the indication of these areas within 3D molecular representations that are responsible for biological or chemical effects. Technically, the latter comes down to the selection or elimination of the reliable variables during 3D QSAR modeling using the Partial Least-Squares (PLS) method. In this paper we used a series of benzoic acids to test the dependence between the predictive ability and variable selection performance of PLS with Iterative Variable Elimination (IVE-PLS) in the Comparative Molecular Surface Analysis (CoMSA) modeling of Hammett constant which correlates with the pKa values. Modeling this chemical effect allowed us to select the IVE-PLS variant that plots the contour maps indicating a carboxylic function, i.e., the region including the dissociation reaction center that determines the respective pKa values. In fact, it appeared that a novel robust IVE version is capable of the indication of the proper contour plots independent of the method used for the calculation of partial atomic charges (AM1 or Gasteiger-Marsili).

Modeling robust QSAR.

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

3D QSAR study of hypolipidemic asarones by comparative molecular surface analysis.

Three-dimensional quantitative structure-activity relationship (3D QSAR) modeled for alpha-asarone derivatives using the comparative molecular surface analysis (CoMSA) allowed us to reveal a correlation between the activity of these compounds and the electrostatic potential at the molecular surface. The grid formalism (s-CoMSA) allowed us to indicate a pharmacophore that is of key importance for compound activity. The CoMSA formalism coupled with the iterative variable elimination method gives a highly predictive model.

Modeling robust QSAR. 1. Coding molecules in 3D-QSAR--from a point to surface sectors and molecular volumes.

Shape analysis is a powerful tool in chemistry and drug design. In the current work, we compare the results of CoMFA and Comparative Molecular Surface Analysis (CoMSA), the 3D-QSAR method, for a series of hypolipidemic and antiplatelet asarones and antifungal N-myristoyltransferase inhibitors. In this publication we show that a sector CoMSA formalism enables an analysis of the biological activity that is more directly related to the molecular shape and individual molecular functionalities than the traditional uniform and directionless CoMFA field. Iterative Variable Elimination allowed us to identify the potential pharmacophoric sites. We modeled QSARs for both series and demonstrate that sector-based molecular descriptors give very predictive models and allow one to generate a spatial interpretation of the QSAR models. In particular, we identified the central aromatic ring and carbonyl functions as the moieties determining the activity of the asarones series, while the pattern of substitution of the aromatic ring determines the activity of N-myristoyltransferase inhibitors.

The comparative molecular surface analysis (CoMSA) with modified uniformative variable elimination-PLS (UVE-PLS) method: application to the steroids binding the aromatase enzyme.

The application of the CoMSA method to analyze 3D QSAR of 50 steroid aromatase inhibitors is described. The 3D QSAR model obtained, reaching a value of cross-validated q(2) = 0.96 (s = 0.31), significantly outperforms those reported in the literature for the CoMFA or CoSA (CoSASA). It is shown that the Uniformative Variable Elimination UVE-PLS or modified iterative UVE procedure (IVE-PLS) can be used for indicating the regions contributing to the binding activity. Thus, after separating the series into two groups of the training and test molecules quite correct external predictions result from the processing of the training set. We proved that the procedure of the data elimination provides stable results, if tested in 50 random runs of the IVE-PLS-CoMSA with different training/test sets. Depending upon the procedure used the quality of the predictions for 25 test molecules is given by SDEP = sum(y(pred)-y(obs))(2)/n)(1/2) = 0.321 - 0.782.

Comparative molecular surface analysis: a novel tool for drug design and molecular diversity studies.

The application of the SOM network in drug design and molecular diversity is discussed. In particular, examples of the applications of the Comparative Molecular Surface Analysis (CoMSA) are reviewed. Molecular surface is a fuzzy category, inspired by the macroscopic world, which has no unique equivalent in the molecular scale. However, it is somewhere near the area where the molecular recognition processes are taking place. Consequently, the methods that analyze this region promise better efficiency than procedures that are based on uniform grids. An important advantage of the CoMSA method is the possibility for the generation of fuzzy molecular representations together with its ability to discover such aspects of molecular similarity that can be easily overlooked by a chemist. The ability for data compression is a further advantage. It has also been shown that the fast processing of the comparative Kohonen mapping enables one to implement this method in the field of molecular diversity.

The comparative molecular surface analysis (COMSA)--a nongrid 3D QSAR method by a coupled neural network and PLS system: predicting pK(a) values of benzoic and alkanoic acids.

A self-organizing neural network was used to design a novel method capable of the quantitative prediction of molecular properties. The method is based on the comparison of molecular surfaces performed by the coupled neural network and PLS system. Unlike CoMFA and related methods it does not compare the properties describing a discrete set of points but the average property values calculated for a certain area of the molecular surface. It has been found that the results of the PLS analysis of the series of the comparative matrices of the molecular electrostatic potential (MEP) are quite stable. Also the results only slightly depend on such parameters as the number of points sampled at the molecular surface (D) or a winning distance (MD) of the self-organizing neurons. The influence of these parameters for modeling the effects limited by steric and electronic effects was determined and the pK(a) values of the ortho-, meta-, and para- (o-, m-, p-) analogues of benzoic acid and selected alkanoic acids were predicted. We generally found that for the series analyzed CoMSA gave better models than CoMFA.

Comparative molecular surface analysis (CoMSA) for modeling dye-fiber affinities of the azo and anthraquinone dyes.

Despite recent investigations aimed at modeling 3D QSAR for dye molecules a controversy still exists: can a pharamacophore hypothesis be used for such purposes. In the present publication we reported on the application of the CoMSA method for modeling 3D QSAR of azo and anthraquinone dyes. We obtained very predictive models, which significantly outperform those reported in the previous CoMFA studies, especially for the azo dyes. Our results proved the previous conclusion that steric requirements are far less pronounced for the cellulose cavities than for the classical drug receptor. Moreover, our results indicate that all molecular surface segments are important for dye-fiber interactions, which also makes an important difference in relation to the classical drug pharmacophore. On the other hand, high predictivity of the CoMSA models indicates that a pharmacophore concept is suitable for the description of the dye-fiber interactions. However, this pharmacophore must substantially differ from the drug pharmacophore used for the illustration of the drug-receptor interactions. From a theoretical point of view dye-cellulose interactions can be an interesting case in which shape decides the activity rules not by the steric repulsion but as a cofactor determining the electrostatic potential distribution.

GRID formalism for the comparative molecular surface analysis: application to the CoMFA benchmark steroids, azo dyes, and HEPT derivatives.

Shape analysis is a powerful tool in chemistry and drug design, and molecular surface defines shape in the molecular scale. In the current publication we presented a novel formalism for the comparative molecular surface analysis (s-CoMSA). The method enables both quantitative modeling of 3D-QSAR and finding possible pharmacophoric sites. The method provides very predictive models for the CBG activity of the benchmark steroid series, tinctorial properties of the heterocyclic azo dyes and anti-HIV activity of the HEPT series.