Isolation of enantiomers via diastereomer crystallisation

Enantiomer separation remains an important technique for obtaining optically active materials. Even though the enantiomers have identical physical properties, the difference in their biological activities make it important to separate them, in order to use single enantiomer products in the pharmaceutical and fine chemical industries. In this project, the separations of three pairs of diastereomer salts (Fig1) by crystallisation are studied, as examples of the ‘classical’ resolution of enantiomers via conversion to diastereomers. The lattice energies of these diastereomer compounds are calculated computationally (based on realistic potentials for the dominant electrostatic interactions and ab initio conformational energies). Then the experimental data are compared with the theoretical data to study the efficiency of the resolving agent. All three fractional crystallisations occurred relatively slowly, and appeared to be thermodynamically controlled. Separabilities by crystallisation have been compared with measured phase equilibrium data for the three systems studied. All crystallisations appear to be consistent with ternary phase diagrams. In the case of R = CH3, where the salt-solvent ternaries exhibited eutonic behaviour, the direction of isomeric enrichment changed abruptly on passing through the eutonic composition. In another example, R = OH, the ternaries indicated near-ideal solubility behaviour of the salt mixtures, and the separation by crystallisation again corresponded. Further, new polymorphic structures and generally better structure predictions have been obtained through out this study. In the case of R = CH3, an improved structure of the p-salt has been determined. In the case of R = C2H5, new polymorphic forms of the n-salts, II and III, have been both discovered and predicted. This work also demonstrates that chemically related organic molecules can exhibit different patterns of the relative energies of the theoretical low energy crystal structures, along with differences in the experimental polymorphic behaviour. This joint experimental and computational investigation provides a stringent test of the reliability of lattice modelling to explain the origins of chiral resolution via diastereomer formation. All the experimental and computational works investigated in this thesis are published (see APPENDIX 1)

Kinetic model construction using chemoinformatics

Kinetic models of chemical processes not only provide an alternative to costly experiments; they also have the potential to accelerate the pace of innovation in developing new chemical processes or in improving existing ones. Kinetic models are most powerful when they reflect the underlying chemistry by incorporating elementary pathways between individual molecules. The downside of this high level of detail is that the complexity and size of the models also steadily increase, such that the models eventually become too difficult to be manually constructed. Instead, computers are programmed to automate the construction of these models, and make use of graph theory to translate chemical entities such as molecules and reactions into computer-understandable representations. This work studies the use of automated methods to construct kinetic models. More particularly, the need to account for the three-dimensional arrangement of atoms in molecules and reactions of kinetic models is investigated and illustrated by two case studies. First of all, the thermal rearrangement of two monoterpenoids, cis- and trans-2-pinanol, is studied. A kinetic model that accounts for the differences in reactivity and selectivity of both pinanol diastereomers is proposed. Secondly, a kinetic model for the pyrolysis of the fuel “JP-10” is constructed and highlights the use of state-of-the-art techniques for the automated estimation of thermochemistry of polycyclic molecules. A new code is developed for the automated construction of kinetic models and takes advantage of the advances made in the field of chemo-informatics to tackle fundamental issues of previous approaches. Novel algorithms are developed for three important aspects of automated construction of kinetic models: the estimation of symmetry of molecules and reactions, the incorporation of stereochemistry in kinetic models, and the estimation of thermochemical and kinetic data using scalable structure-property methods. Finally, the application of the code is illustrated by the automated construction of a kinetic model for alkylsulfide pyrolysis

Extraction of chemical structures and reactions from the literature

The ever increasing quantity of chemical literature necessitates the creation of automated techniques for extracting relevant information. This work focuses on two aspects: the conversion of chemical names to computer readable structure representations and the extraction of chemical reactions from text. Chemical names are a common way of communicating chemical structure information. OPSIN (Open Parser for Systematic IUPAC Nomenclature), an open source, freely available algorithm for converting chemical names to structures was developed. OPSIN employs a regular grammar to direct tokenisation and parsing leading to the generation of an XML parse tree. Nomenclature operations are applied successively to the tree with many requiring the manipulation of an in-memory connection table representation of the structure under construction. Areas of nomenclature supported are described with attention being drawn to difficulties that may be encountered in name to structure conversion. Results on sets of generated names and names extracted from patents are presented. On generated names, recall of between 96.2% and 99.0% was achieved with a lower bound of 97.9% on precision with all results either being comparable or superior to the tested commercial solutions. On the patent names OPSIN s recall was 2-10% higher than the tested solutions when the patent names were processed as found in the patents. The uses of OPSIN as a web service and as a tool for identifying chemical names in text are shown to demonstrate the direct utility of this algorithm. A software system for extracting chemical reactions from the text of chemical patents was developed. The system relies on the output of ChemicalTagger, a tool for tagging words and identifying phrases of importance in experimental chemistry text. Improvements to this tool required to facilitate this task are documented. The structure of chemical entities are where possible determined using OPSIN in conjunction with a dictionary of name to structure relationships. Extracted reactions are atom mapped to confirm that they are chemically consistent. 424,621 atom mapped reactions were extracted from 65,034 organic chemistry USPTO patents. On a sample of 100 of these extracted reactions chemical entities were identified with 96.4% recall and 88.9% precision. Quantities could be associated with reagents in 98.8% of cases and 64.9% of cases for products whilst the correct role was assigned to chemical entities in 91.8% of cases. Qualitatively the system captured the essence of the reaction in 95% of cases. This system is expected to be useful in the creation of searchable databases of reactions from chemical patents and in facilitating analysis of the properties of large populations of reactions

The self-assembly of metallosupramolecular architectures

This thesis describes the design, synthesis and characterization of metallosupramolecular cleft and box-shaped molecules. These compounds are generated via the self-assembly of building block compounds; metallated thioether complexes and aronatic N-donor ligands. Chapter Two describes the synthesis and characterization of the building block compounds. Cyclometallation of the thioether ligand 1,3-bis(phenylthio-methyl)benzene (PhS\sb2) with (Pd(CH\sb3CN)\sb4) (BF\sb4\rbrack\sb2 produced the species (Pd(PhS\sb2)(CH\sb3CN)) (BF\sb4). By varying the S-R group, a family of compounds were synthesized each possessing different physical properties. The metallated ligands exhibited inversion of sulfur and this was studied via variable temperature \sp1H NMR spectroscopy. As well, several ligands of the form 1,2,4,5-tetrakis-(phenylthiomethyl)benzene (PhS\sb4) were synthesized. These ligands underwent the cyclometallation reaction twice, forming the doubly palladated compound (Pd\sb2(PhS\sb4)(CH\sb3CN)\sb2\rbrack (BF\sb4\rbrack\sb2, In an attempt to produce a trigonal bipyramidal Pd(II) complex with 1,10-phenanthroline, an uncommon square pyramidal geometry was produced. These building block compounds were characterized through the use of \sp{13}C\{\sp1H$\}$ and \sp1H NMR spectroscopy as well as X-ray crystallography. Chapter Three describes the synthesis of molecular clefts and boxes from the building block compounds developed in Chapter Two. When mixed in a ratio of 2:1, (Pd(PhS\sb2)(CH\sb3CN)) (BF\sb4):4,7-phenanthroline, the molecular cleft ((Pd(PhS\sb2))\sb2(4,7-phen)) (BF\sb4\rbrack\sb2 (Ph-Cleft) can be isolated in quantitative yields. This was found to be the case with the n-butyl version of the ligand as well. The self-assembly of a molecular box was achieved by the addition of (Pd\sb2(PhS\sb4)(CH\sb3CN)\sb2) (BF\sb4\rbrack\sb2 to 4,7-phenanthroline in a 1:1 ratio. The sole product of this reaction is the molecular box, a cyclic complex containing three wall units, (Pd\sb2(PhS\sb4)) (BF\sb4\rbrack\sb2 and three corner pieces; 4,7-phenanthroline. Similar results were obtained utilizing the (Pd\sb2(BuS\sb4)(CH\sb3CN)\sb2) (BF\sb4\rbrack\sb2 compound with 4,7-phenanthroline. These supramolecular species were characterized by \sp1H NMR and mass spectrometry as well as X-ray crystallography.Dept. of Chemistry and Biochemistry. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1996 .H35. Source: Masters Abstracts International, Volume: 37-01, page: 0266. Adviser: Stephen Loeb. Thesis (M.Sc.)--University of Windsor (Canada), 1996

Cheminformatics for genome-scale metabolic reconstructions

Genome-scale metabolic reconstructions are an important resource in the study of metabolism. They provide both a system and component level view of the biochemical transformations of metabolites. As more reconstructions have been created it remains a challenge to integrate and reason about their contents. This thesis focuses on the development of computational methods to allow on-demand comparison and alignment of metabolic reconstructions. A novel method is introduced that utilises chemical structure representations to identify equivalent metabolites between reconstructions. Using a graph theoretic representation allows the identification and reasoning of metabolites that have a non-exact match. A key advantage is that the method uses the contents of reconstructions directly and does not rely on the creation or use of a common reference. To annotate reconstructions with chemical structure representations an interactive desktop application is introduced. The application assists in the creation and curation of metabolic information using manual, semi-auto\-mated, and automated methods. Chemical structure representations can be retrieved, drawn, or generated to allow precise metabolite annotation. In processing chemical information, efficient and optimised algorithms are required. Several areas are addressed and implementations have been contributed to the Chemistry Development Kit. Rings are a fundamental property of chemical structures therefore multiple ring definitions and fast algorithms are explored. Conversion and standardisation between structure representations present a challenge. Efficient algorithms to determine aromaticity, assign a Kekulé form, and generate tautomers are detailed. Many enzymes are selective and specific to stereochemistry. Methods for the identification, depiction, comparison, and description of stereochemistry are described.The project was funded by Unilever, the Biotechnology and Biological Sciences Research Council [BB/I532153/1], and the European Molecular Biology Laboratory

ENZYMES: Catalysis, Kinetics and Mechanisms

Onemarvelsattheintricate designoflivingsystems,andwecannotbutwonderhow life originated on this planet. Whether ?rst biological structures emerged as the selfreproducing genetic templates (genetics-?rst origin of life) or the metabolic universality preceded the genome and eventually integrated it (metabolism-?rst origin of life) is still a matter of hot scienti?c debate. There is growing acceptance that the RNA world came ?rst – as RNA molecules can perform both the functions of information storage and catalysis. Regardless of which view eventually gains acceptance, emergence of catalytic phenomena is at the core of biology. The last century has seen an explosive growth in our understanding of biological systems. The progression has involved successive emphasis on taxonomy ! physiology ! biochemistry ! molecular biology ! genetic engineering and ?nally the large-scale study of genomes. The ?eld of molecular biology became largely synonymous with the study of DNA – the genetic material. Molecular biology however had its beginnings in the understanding of biomolecular structure and function. Appreciationofproteins,catalyticphenomena,andthefunctionofenzymeshadalargeroleto play in the progress of modern biology

Enhancing Reaction-based de novo Design using Machine Learning

De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources