Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Decentralized load balancing for highly irregular search problems

In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

Synthesis and characterization of carbamoyl methyl pyrazole(CMPz) compounds of palladium(II) chloride: the crystal and molecular structure of [PdCl2{(C3H3N2CH2CONBu2)-Bu-i}(2)]

Carbamoyl methyl pyrazole compound of palladium(II) chloride of the type [PdCl2L2] (where L = C5H7N2CH2CON(C4H9)(2), C5H7N2CH2CON((C4H9)-C-i)(2), C3H3N2CH2CON(C4H9)(2), or C3H3N2CH2CON((C4H9)-C-i)(2)) has been synthesized and characterized by IR and H-1 NMR spectroscopy. The structure of the compound [PdCl2{(C3H3N2CH2CONBu2}2)-Bu-i] has been determined by single crystal X-ray diffraction and shows that the ligands are bonded through the soft pyrazolyl nitrogen atom to the palladium(II) chloride in a trans disposition. (c) 2007 Elsevier B.V. All rights reserved.

Role of polyhydroxybutyrate and glycogen as carbon storage compounds in pea and bean bacteroids

Rhizobium leguminosarum synthesizes polyhydroxybutyrate and glycogen as its main carbon storage compounds. To examine the role of these compounds in bacteroid development and in symbiotic efficiency, single and double mutants of R. legumosarum bv. viciae were made which lack polyhydroxybutyrate synthase (phaC), glycogen synthase (glgA), or both. For comparison, a single phaC mutant also was isolated in a bean-nodulating strain of R. leguminosarum bv. phaseoli. In one large glasshouse trial, the growth of pea plants inoculated with the R. leguminosarum bv. viciae phaC mutant were significantly reduced compared with wild-type-inoculated plants. However, in subsequent glasshouse and growth-room studies, the growth of pea plants inoculated with the mutant were similar to wildtype-inoculated plants. Bean plants were unaffected by the loss of polyhydroxybutyrate biosynthesis in bacteroids. Pea plants nodulated by a glycogen synthase mutants or the glgA/phaC double mutant, grew as well as the wild type in growth-room experiments. Light and electron micrographs revealed that pea nodules infected with the glgA mutant accumulated large amounts of starch in the II/III interzone. This suggests that glycogen may be the dominant carbon storage compound in pea bacteroids. Polyhydroxybutyrate was present in bacteria in the infection thread of pea plants but was broken down during bacteroid formation. In nodules infected with a phaC mutant of R. leguminosarum bv. viciae, there was a drop in the amount of starch in the II/III interzone, where bacteroids form. Therefore, we propose a carbon burst hypothesis for bacteroid formation, where polyhydroxybutyrate accumulated by bacteria is degraded to fuel bacteroid differentiation.

Role of polyhydroxybutyrate and glycogen as carbon storage compounds in pea and bean bacteroids

Rhizobium leguminosarum synthesizes polyhydroxybutyrate and glycogen as its main carbon storage compounds. To examine the role of these compounds in bacteroid development and in symbiotic efficiency, single and double mutants of R. legumosarum bv. viciae were made which lack polyhydroxybutyrate synthase (phaC), glycogen synthase (glgA), or both. For comparison, a single phaC mutant also was isolated in a bean-nodulating strain of R. leguminosarum bv. phaseoli. In one large glasshouse trial, the growth of pea plants inoculated with the R. leguminosarum bv. viciae phaC mutant were significantly reduced compared with wild-type-inoculated plants. However, in subsequent glasshouse and growth-room studies, the growth of pea plants inoculated with the mutant were similar to wildtype-inoculated plants. Bean plants were unaffected by the loss of polyhydroxybutyrate biosynthesis in bacteroids. Pea plants nodulated by a glycogen synthase mutants or the glgA/phaC double mutant, grew as well as the wild type in growth-room experiments. Light and electron micrographs revealed that pea nodules infected with the glgA mutant accumulated large amounts of starch in the II/III interzone. This suggests that glycogen may be the dominant carbon storage compound in pea bacteroids. Polyhydroxybutyrate was present in bacteria in the infection thread of pea plants but was broken down during bacteroid formation. In nodules infected with a phaC mutant of R. leguminosarum bv. viciae, there was a drop in the amount of starch in the II/III interzone, where bacteroids form. Therefore, we propose a carbon burst hypothesis for bacteroid formation, where polyhydroxybutyrate accumulated by bacteria is degraded to fuel bacteroid differentiation.

Molecular structures of benzoic acid and 2-hydroxybenzoic acid, obtained by gas-phase electron diffraction and theoretical calculations

The structures of benzoic acid (C6H5COOH) and 2-hydroxybenzoic acid (C6H4OHCOOH) have been determined in the gas phase by electron diffraction using results from quantum chemical calculations to inform restraints used on the structural parameters. Theoretical methods (HF and MP2/6-311+G(d, p)) predict two conformers for benzoic acid, one which is 25.0 kJ mol(-1) (MP2) lower in energy than the other. In the low-energy form, the carboxyl group is coplanar with the phenyl ring and the O-H group eclipses the C=O bond. Theoretical calculations (HF and MP2/6-311+ G(d, p)) carried out for 2-hydroxybenzoic acid gave evidence for seven stable conformers but one low-energy form (11.7 kJ mol-1 lower in energy (MP2)) which again has the carboxyl group coplanar with the phenyl ring, the O-H of the carboxyl group eclipsing the C=O bond and the C=O of the carboxyl group oriented toward the O-H group of the phenyl ring. The effects of internal hydrogen bonding in 2-hydroxybenzoic acid can be clearly observed by comparison of pertinent structural parameters between the two compounds. These differences for 2-hydroxybenzoic acid include a shorter exocyclic C-C bond, a lengthening of the ring C-C bond between the substituents, and a shortening of the carboxylic single C-O bond.

Copper(II) complexes of mono-anionic glutamate: anionic influence in the variations of molecular and supramolecular structures

Three new polynuclear copper(II) complexes of singly deprotonated L-glutamic acid (L-glu), {[Cu(bipy)(2)][Cu(bipy)(L-glu)H2O](2)(BF4)(4)center dot(H2O)(3)}(n) (1), {[Cu(bipy)(L-glu)H2O][Cu(bipy)(L-glu)(ClO4)]( ClO4)center dot(H2O)(2)}(n) ((2)) and [Cu(phen)(L-glu)H2O](2)(NO3)(2)center dot(H2O)(4) (3) (bipy = 2,2-bipyridine, phen = 1,10-phenanthroline), were synthesized in acidic pH (ca. 2.5) and characterized structurally. In all the complexes, L-glutamic acid acts as a bidentate chelating ligand, leaving the protonated carboxylic acid free. Both in 1 and 2, two different types of species [Cu(bipy)(2)](BF4)(2) and [Cu(bipy)(L-glu)H2O] BF4 for 1 and [Cu(bipy)(L-glu)H2O]ClO4 and [Cu(bipy)(L-glu)(ClO4)] for 2 coexist in the solid state. In complex 1, the [C( bipy)(L-glu)H2O]+ units are joined together by syn-anti carboxylate bridges to form an enantiopure (M) helical chain and the [Cu(bipy)(2)](2+) presents a very rare example of the four-coordinate distorted tetrahedral geometry of Cu(II). In complex 2, the [Cu(bipy)(L gluClO(4))] units are joined together by weakly coordinating perchlorate ions to form a 1D polymeric chain while the [Cu(bipy)(L-glu)H2O]+ units remain as mononuclear species. The different coordinating ability of the two counter anions along with their involvement in the H-bonding network seems likely to be responsible for the difference in the final polymeric structures in the two compounds. Variable-temperature (2-300 K) magnetic susceptibility measurements show negligible coupling for both the complexes. The structure of 3 consists of two independent monomeric [Cu(phen)(L-glu)H2O]+ cations, two nitrate anions and four water molecules. The copper atom occupies a five-coordinate square pyramidal environment with a water molecule in the axial position.

A DFT study of free radicals formed from artemisinin and related compounds

Ab initio calculations using density functional theory have shown that the reactions that occur between artemisinin, 1, a cyclic trioxane active against malaria, and some metal ions and complexes lead to a series of radicals which are probably responsible for its therapeutic activity. In particular it has been shown that the interaction of Fe(H) with artemisinin causes the O-O bond to be broken as indeed does Fe(III) and Cu(I), while Zn(II) does not. Calculations were carried out with Fe(II) in several different forms including the bare ion, [Fe(H2O)(5)](2+) and [FeP(Im)] (P, porphyrin; Im, imadazole) and similar results were obtained. The resulting oxygen-based radicals are readily converted to more stable carbon-based radicals and/or. stable products. Similar radicals and products are also formed from two simple model trioxanes 2 and 3 that show little or no therapeutic action against malaria although some subtle differences were obtained. This suggests that the scaffold surrounding the pharmacophore may be involved in molecular recognition events allowing efficient uptake of this trioxane warhead into the parasite. (C) 2004 Elsevier B.V. All rights reserved.