The dye-sensitized solar cell database.

BACKGROUND: Dye-sensitized solar cells (DSSCs) have garnered a lot of attention in recent years. The solar energy to power conversion efficiency of a DSSC is influenced by various components of the cell such as the dye, electrolyte, electrodes and additives among others leading to varying experimental configurations. A large number of metal-based and metal-free dye sensitizers have now been reported and tools using such data to indicate new directions for design and development are on the rise. DESCRIPTION: DSSCDB, the first of its kind dye-sensitized solar cell database, aims to provide users with up-to-date information from publications on the molecular structures of the dyes, experimental details and reported measurements (efficiencies and spectral properties) and thereby facilitate a comprehensive and critical evaluation of the data. Currently, the DSSCDB contains over 4000 experimental observations spanning multiple dye classes such as triphenylamines, carbazoles, coumarins, phenothiazines, ruthenium and porphyrins. CONCLUSION: The DSSCDB offers a web-based, comprehensive source of property data for dye sensitized solar cells. Access to the database is available through the following URL: www.dyedb.com .

Automated building of organometallic complexes from 3D fragments.

A method for the automated construction of three-dimensional (3D) molecular models of organometallic species in design studies is described. Molecular structure fragments derived from crystallographic structures and accurate molecular-level calculations are used as 3D building blocks in the construction of multiple molecular models of analogous compounds. The method allows for precise control of stereochemistry and geometrical features that may otherwise be very challenging, or even impossible, to achieve with commonly available generators of 3D chemical structures. The new method was tested in the construction of three sets of active or metastable organometallic species of catalytic reactions in the homogeneous phase. The performance of the method was compared with those of commonly available methods for automated generation of 3D models, demonstrating higher accuracy of the prepared 3D models in general, and, in particular, a much wider range with respect to the kind of chemical structures that can be built automatically, with capabilities far beyond standard organic and main-group chemistry.

Automated design of realistic organometallic molecules from fragments.

A method for the automated generation of realistic, synthetically accessible transition metal and organometallic complexes is described. Computational tools were designed to generate molecular fragments, preferably harvested from libraries of existing, stable compounds, to be used as building blocks for the construction of new molecules. These fragments are enriched with information about the number and type of possible connections to other fragments and are stored in library files. When connecting fragments in the subsequent building process, compatibility matrices, which define the connection rules between fragments, are used to delineate organometallic fragment spaces from which molecules can be generated in an automated fashion. The approach is flexible and allows ample structural variation at the same time as the combination of known fragments is easily restrained to avoid generation of exotic and unrealistic substructures and molecules. The method was tested in the generation of ruthenium complexes, with a given coordination environment, which can serve as candidates in catalyst development. The results demonstrate that molecules generated with the described method do not contain exotic arrangements of atoms and are by far more realistic than those obtained by the application of valence rules alone.

Rapid localization of bone fragments on surfaces using back-projection and hyperspectral imaging.

Manual localization of bone fragments on the ground or on complex surfaces in relation to accidents or criminal activity may be time-consuming and challenging. It is here investigated whether combining a near-infrared hyperspectral camera and chemometric modeling with false color back-projection can be used for rapid localization of bone fragments. The approach is noninvasive and highlights the spatial distribution of various compounds/properties to facilitate manual inspection of surfaces. Discriminant partial least squares regression is used to classify between bone and nonbone spectra from the hyperspectral camera. A predictive model (>95% prediction ability) is constructed from raw chicken bones mixed with stone, sand, leaves, moss, and wood. The model uses features in the near-infrared spectrum which may be selective for bones in general and is able to identify a wide variety of bones from different animals and contexts, including aged and weathered bone.

An evolutionary algorithm for de novo optimization of functional transition metal compounds.

Development of functional inorganic and transition metal compounds is usually based on ad hoc qualified guesses, with computational methods playing a lesser role than in drug discovery. A de novo evolutionary algorithm (EA) is presented that automatically generates transition metal complexes using a search space constrained around chemically meaningful structures assembled from three kinds of fragments: a part shared by all structures and typically containing the metal center itself, one or several parts consisting of ligand skeletons, and unconstrained parts that may grow and vary freely. In EA optimizations, using a cost-efficient fitness function based on a linear quantitative structure-activity relationship model for catalytic activity, we demonstrate the capabilities of the method by retracing the transition from the first-generation, phosphine-based Grubbs olefin metathesis catalysts to second-generation catalysts containing N-heterocyclic carbene ligands instead of phosphines. Moreover, DFT calculations on selected high-fitness, last-generation structures from these evolutionary experiments suggest that, in terms of catalytic activity, the structures arrived at by virtual evolution alone compare favorably with existing, highly active catalysts. The structures from the evolution experiments are, however, complex and probably difficult to synthesize, but a set of manually simplified variations thereof might form the leads for a new generation of Grubbs catalysts.

PryJector: a device for in situ visualization of chemical and physical property distributions on surfaces using projection and hyperspectral imaging.

Traditional forensic methods that highlight the spatial distribution of properties such as blood and fingerprints have two main disadvantages: they often apply chemicals that may influence further analyses, and they cannot easily be modified to search for new compounds/properties. A new instrument (called PryJector) avoids these problems by dynamically projecting back onto the surface under study spatially distributed information of compounds/properties (chemical images) obtained from multivariate analysis of hyperspectral images. Selectivity to target compounds/properties is ensured by multivariate modeling which makes the instrument much more flexible compared to traditional methods. The functionality of the PryJector is demonstrated in an application related to the detection of counterfeit pharmaceuticals where compounds otherwise indistinguishable to the human eye are made clearly visible by projection of false-colored chemical images. The PryJector is shown to be a noninvasive and very flexible instrument for highlighting spatial distributions of various compounds/properties.

A new method for assigning common spot boundaries for multiple gels in two-dimensional gel electrophoresis.

The benefits of defining common spot boundaries when several gels from 2-DE are compared and analyzed have lately been stressed by both commercial software producers and users of this software. Though the importance of common spot boundaries is clearly stated, few reports exist that target this issue explicitly. In this study a method for defining common spots boundaries is developed, called the spot density method. The method consists of the following steps: segmentation and spot identification on each individual gel, transferring the spot-center coordinates for all gels onto a single new gel, collecting spot centers clustered together in the new gel and finally assigning pixels and new spot boundaries based on the spots in each cluster. The method is compared to a synthetic gel approach, and validated by visual inspection of three representative areas in the gels. The gel images need to be aligned prior to segmentation and spot identification, but the method can be used regardless of the choice of segmentation procedure. This makes the method an easy extension to existing methods for spot identification and matching. Conclusions based on the visual inspection are that the spot density method identifies partly overlapping spots and low-intensity spots better than the synthetic gel approach.

A multivariate spot filtering model for two-dimensional gel electrophoresis.

Image segmentation plays an important role in the automatic analysis of protein spots in 2-DE. Using image segments representing protein spots, the amount of protein in each segment can be quantified, and corresponding segments can be matched and compared for multiple gels. However, the common presence of image segments caused by noise and unwanted artefacts highly disturb the analysis and comparison of the gels. Common sources of such artefacts are cracks in the gel surface, fingerprints, dust and other pollutions. It would be advantageous to remove these unwanted artefacts during or after the segmentation procedure. To achieve this task a multivariate spot filtering model is developed using image segments from a gel segmentation. Parameters in the model are based on texture, shape and intensity measurements of the image segments. The model successfully managed to separate segments caused by noise, artefacts and cracks from image segments representing true protein spots. The classification method used is discriminant partial least squares regression.

An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis.

An improved pixel-based approach for analyzing 2-DE images is presented. The key feature of the method is to create a mask based on all gels in the experiment using image morphology, followed by multivariate analysis on the pixel level. The method reduces the impact of noise and background by identifying regions in the image where protein spots are present, but make no assumption on individual spot boundaries for isolated spots. This makes it possible to detect significant changes in complex regions, and visualize these changes over multiple gels in an easy way. False missing values and spot volumes caused by imposing erroneous spot boundaries are thus circumvented. The approach presented gives improved pixel-based information from the gels, and is also an alternative to existing methods for data-reduction, significance testing and visualization of 2-DE data. Results are compared with software using a common spot boundary approach on an experiment consisting of 35 full size gel images. Gel alignment is required before analysis.

A framework for significance analysis of gene expression data using dimension reduction methods.

BACKGROUND: The most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, identification of features that correlate with continuous dependent variables is more difficult using these methods, and long lists of significant genes returned are not easily probed for co-regulations and dependencies. Dimension reduction methods are much used in the microarray literature for classification or for obtaining low-dimensional representations of data sets. These methods have an additional interpretation strength that is often not fully exploited when expression data are analysed. In addition, significance analysis may be performed directly on the model parameters to find genes that are important for any number of categorical or continuous responses. We introduce a general scheme for analysis of expression data that combines significance testing with the interpretative advantages of the dimension reduction methods. This approach is applicable both for explorative analysis and for classification and regression problems. RESULTS: Three public data sets are analysed. One is used for classification, one contains spiked-in transcripts of known concentrations, and one represents a regression problem with several measured responses. Model-based significance analysis is performed using a modified version of Hotelling's T2-test, and a false discovery rate significance level is estimated by resampling. Our results show that underlying biological phenomena and unknown relationships in the data can be detected by a simple visual interpretation of the model parameters. It is also found that measured phenotypic responses may model the expression data more accurately than if the design-parameters are used as input. For the classification data, our method finds much the same genes as the standard methods, in addition to some extra which are shown to be biologically relevant. The list of spiked-in genes is also reproduced with high accuracy. CONCLUSION: The dimension reduction methods are versatile tools that may also be used for significance testing. Visual inspection of model components is useful for interpretation, and the methodology is the same whether the goal is classification, prediction of responses, feature selection or exploration of a data set. The presented framework is conceptually and algorithmically simple, and a Matlab toolbox (Mathworks Inc, USA) is supplemented.

Quantitative prediction of the absorption maxima of azobenzene dyes from bond lengths and critical points in the electron density.

The relationship between the molecular electronic structure and the position of the absorption maxima in 191 azobenzene dyes has been studied by quantitative structure-property relations. A strong linearity is observed between the nitrogen-nitrogen bond length and the absorption wavelength with a squared correlation coefficient of 0.90. Bond lengths and properties of the critical points located on the electron density distribution are used to build partial least squares regression models for quantitative prediction of absorption wavelengths. Fifty of the azobenzene dyes were used as an external test set to evaluate the overall performance of the models. The simplest model where only the nitrogen-nitrogen bond length is used as a descriptor gives a root mean square error of prediction of 12.6 nm. When the value, laplacian and ellipticity of the electron density in all comparable bond critical points are used, the error of prediction is reduced to 5.4 nm. However, this model is less general and robust to prediction of novel molecular structures. It is demonstrated that the nitrogen-nitrogen bond in the azobenzene compounds relates to the colour of the dyes and in particular the nitrogen-nitrogen bond length plays a central role.

Validation of critical points in the electron density as descriptors by building quantitative structure-property relationships for the atomic polar tensor.

A crucial component of research in the field of quantitative structure-activity/property relationships is the identification of molecular descriptors relevant to the activity or property of interest. Descriptors based on the topology of the electron density as formulated in Bader's theory of atoms in molecules are investigated in detail in this work. In a model study, the authors investigate their ability to predict the atomic polar tensor (the gradient of the molecular dipole moment), which contains information on the vibrational intensities in infrared spectroscopy and constitutes a scheme for partitioning the total charge distribution into atomic charges. The atomic polar tensor may therefore be used to investigate whether the descriptors give adequate information on the local electronic structure in the molecule. Both the trace of the atomic polar tensor and for planar molecules its out-of-plane component may be interpreted as definitions of atomic charges suitable for prediction. Hydrogen and carbon atoms in a set of 60 aromatic compounds with various substituents have been studied. Excellent results for prediction of hydrogen and carbon charges have been achieved with cross-validated squared correlation coefficients between predicted and theoretical values varying from 0.92 and 0.977 for the most complex set of substituents when the value, Laplacian, and ellipticity of the electron density in the bond critical points are used as descriptors. The carbon charges defined from the trace of the atomic polar tensor are correlated with its out-of-plane component whereas such relationship is not observed for the hydrogen charges studied in this work.

Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships.

The requirement of aligning each individual molecule in a data set severely limits the type of molecules which can be analysed with traditional structure activity relationship (SAR) methods. A method which solves this problem by using relations between objects is inductive logic programming (ILP). Another advantage of this methodology is its ability to include background knowledge as 1st-order logic. However, previous molecular ILP representations have not been effective in describing the electronic structure of molecules. We present a more unified and comprehensive representation based on Richard Bader's quantum topological atoms in molecules (AIM) theory where critical points in the electron density are connected through a network. AIM theory provides a wealth of chemical information about individual atoms and their bond connections enabling a more flexible and chemically relevant representation. To obtain even more relevant rules with higher coverage, we apply manual postprocessing and interpretation of ILP rules. We have tested the usefulness of the new representation in SAR modelling on classifying compounds of low/high mutagenicity and on a set of factor Xa inhibitors of high and low affinity.

New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors.

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.