Elucidating chemical reactivity by pattern recognition methods

Methods have been developed that allow important chemical effects to be quantified. Parameters calculated with these procedures can be used to investigate both quantitative and qualitative information on chemical reactivity. A variety of statistical and pattern recognition methods is used for that purpose. These studies lead to reactivity functions that allow the prediction of the course of complex organic reactions.     

Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis

The emergence of multidrug resistant varieties of Mycobacterium tuberculosis has led to a search for novel drug targets. We have performed an insilico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen M. tuberculosis. Enzymes from the biochemical pathways of M. tuberculosis from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. We have identified six pathways unique to the pathogen M. tuberculosis when compared to the host H. sapiens. Potential drug targets from these pathways could be useful for the discovery of broad spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 185 distinct targets identified from these pathways, many are in various stages of progress at the TB Structural Genomics Consortium. However, 67 of our targets are new and can be considered for rational drug design. As a case study, we have built a homology model of one of the potential drug targets MurD ligase using WHAT IF software. The model could be further explored for insilico docking studies with suitable inhibitors. The study was successful in listing out potential drug targets from the M. tuberculosis proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatic methods can be extended to other pathogens of clinical interest.     

Toxication of Foreign Substances by Conjugation Reactions

Elucidation of the metabolic pathways of foreign compounds in mammalian organisms contributes substantially to the understanding of toxic effects and is therefore a basic component of every risk analysis. The abundance of chemical reactions which take part in metabolic transformations allows one to speculate that we, with our present state of knowledge, are just at the beginning of a development which will help explain the interplay between chemical structure, biochemical transformation and toxic effects. This applies in particular to the conjugation of foreign compounds with structures and molecules endogenous to the body. Until recently it was thought that these conjugation reactions lead to chemically and biologically inert products, which could be easily eliminated by the organism. Using new biological testing procedures and sensitive methods of analysis, this assumption has been refuted. The fact is, that highly toxic, mutagenic and carcinogenic products can result from the chemical interactions of foreign substances with endogenous substrates.     

Chemistry in Times of Artificial Intelligence

Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.     

Designs for life: protocell models in the laboratory

Compartmentalization of primitive biochemical reactions within membrane-bound water micro-droplets is considered an essential step in the origin of life. In the absence of complex biochemical machinery, the hypothetical precursors to the first biological cells (protocells) would be dependent on the self-organization of their components and physicochemical conditions of the environment to attain a basic level of autonomy and evolutionary viability. Many researchers consider the self-organization of lipid and fatty acid molecules into bilayer vesicles as a simple form of membrane-based compartmentalization that can be developed for the experimental design and construction of plausible protocell models. In this tutorial review, we highlight some of the recent advances and issues concerning the construction of simple cell-like systems in the laboratory. Overcoming many of the current scientific challenges should lead to new types of chemical bio-reactors and artificial cell-like entities, and bring new insights concerning the possible pathways responsible for the origin of life.     

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.     

SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations.Examples of reaction centre detection in Metabolite? are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10^?1 to 10^?5 and their order of magnitude reflected the known and experimentally observed metabolites.SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite?. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure–activity and physiochemical information.     

Organic Chemistry on Solid Supports

Until recently, repetitive solid-phase synthesis procedures were used predominantly for the preparation of oligomers such as peptides, oligosaccharides, peptoids, oligocarbamates, peptide vinylogues, oligomers of pyrrolin-4-one, peptide phosphates, and peptide nucleic acids. However, the oligomers thus produced have a limited range of possible backbone structures due to the restricted number of building blocks and synthetic techniques available. Biologically active compounds of this type are generally not suitable as therapeutic agents but can serve as lead structures for optimization. “Combinatorial organic synthesis” has been developed with the aim of obtaining low molecular weight compounds by pathways other than those of oligomer synthesis. This concept was first described in 1971 by Ugi.^[56f,g,59c] Combinatorial synthesis offers new strategies for preparing diverse molecules, which can then be screened to provide lead structures. Combinatorial chemistry is compatible with both solution-phase and solid-phase synthesis. Moreover, this approach is conducive to automation, as proven by recent successes in the synthesis of peptide libraries. These developments have led to a renaissance in solid-phase organic synthesis (SPOS), which has been in use since the 1970s. Fully automated combinatorial chemistry relies not only on the testing and optimization of known chemical reactions on solid supports, but also on the development of highly efficient techniques for simultaneous multiple syntheses. Almost all of the standard reactions in organic chemistry can be carried out using suitable supports, anchors, and protecting groups with all the advantages of solid-phase synthesis, which until now have been exploited only sporadically by synthetic organic chemists. Among the reported organic reactions developed on solid supports are Diels–Alder reactions, 1,3-dipolar cycloadditions, Wittig and Wittig–Horner reactions, Michael additions, oxidations, reductions, and Pd-catalyzed C? C bond formation. In this article we present a comprehensive review of the previously published solid-phase syntheses of nonpeptidic organic compounds.     

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less‐active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high‐throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high‐throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.     

Single-molecule analysis using DNA origami

During the last two decades, scientists have developed various methods that allow the detection and manipulation of single molecules, which have also been called "in singulo" approaches. Fundamental understanding of biochemical reactions, folding of biomolecules, and the screening of drugs were achieved by using these methods. Single-molecule analysis was also performed in the field of DNA nanotechnology, mainly by using atomic force microscopy. However, until recently, the approaches used commonly in nanotechnology adopted structures with a dimension of 10-20 nm, which is not suitable for many applications. The recent development of scaffolded DNA origami by Rothemund made it possible for the construction of larger defined assemblies. One of the most salient features of the origami method is the precise addressability of the structures formed: Each staple can serve as an attachment point for different kinds of nanoobjects. Thus, the method is suitable for the precise positioning of various functionalities and for the single-molecule analysis of many chemical and biochemical processes. Here we summarize recent progress in the area of single-molecule analysis using DNA origami and discuss the future directions of this research.     

Amines as Catalysts: Dynamic Features and Kinetic Control of Catalytic Asymmetric Chemical Transformations to Form C−C Bonds and Complex Molecules

Carbonyl transformations involving enolates and/or enamines have been used for various types of bond-forming reactions. In this account, catalysts and catalyst systems that have amino acids or primary, secondary, and/or tertiary amines as key catalytic functional groups that we have developed to accelerate chemical transformations, including regio-, diastereo- and enantioselective reactions, are discussed. Our chemical transformation strategies and methods that use amine derivatives as catalysts are also discussed. As amines can have different functions depending on protonation and on the species formed during the catalysis (such as enamines and iminium ions), dynamics and kinetic controls are the keys for understanding the catalysis. Further, strategies that harness dynamic steps and kinetic control in amine-catalyzed reactions have enabled the synthesis of complex molecules in stereocontrolled manners. Understanding the dynamic features and the kinetic controls of the catalysis will further the design of the catalysts and the development of chemical transformation strategies and methods.     

Liquid-phase catalytic processing of biomass-derived oxygenated hydrocarbons to fuels and chemicals

Biomass has the potential to serve as a sustainable source of energy and organic carbon for our industrialized society. The focus of this Review is to present an overview of chemical catalytic transformations of biomass-derived oxygenated feedstocks (primarily sugars and sugar-alcohols) in the liquid phase to value-added chemicals and fuels, with specific examples emphasizing the development of catalytic processes based on an understanding of the fundamental reaction chemistry. The key reactions involved in the processing of biomass are hydrolysis, dehydration, isomerization, aldol condensation, reforming, hydrogenation, and oxidation. Further, it is discussed how ideas based on fundamental chemical and catalytic concepts lead to strategies for the control of reaction pathways and process conditions to produce H(2)/CO(2) or H(2)/CO gas mixtures by aqueous-phase reforming, to produce furan compounds by selective dehydration of carbohydrates, and to produce liquid alkanes by the combination of aldol condensation and dehydration/hydrogenation processes.     

Biomimetic modeling of oxidative drug metabolism

The prediction of drug metabolism is an important task in drug development. Besides well-established in vitro and in vivo methods using biological matrices, several biomimetic models have been developed. This review summarizes three different nonenzymatic strategies, including metalloporphyrins as surrogates of the active centre of cytochrome P450, Fenton’s reagent, and the electrochemical oxidation of drug compounds. Although none of the systems can simulate the whole range of cytochrome P450-catalyzed reactions adequately, the biomimetic models show some advantages over standard in vitro methods. For example, metalloporpyhrin catalysts allow the synthesis of certain metabolites in sufficient amounts and with sufficient purities to permit characterization and further pharmacological and toxicological tests. The electrochemical generation of metabolites coupled on-line to liquid chromatography/mass spectrometry is a promising tool for studying reactive metabolites and can be applied in automated high-throughput screening approaches. In this paper, detailed comparisons with cytochrome P450 catalysis are drawn, advantages and disadvantages of the respective methods are revealed, and possible applications are discussed.     

Construction of N-Heterocycles through Cyclization of Tertiary Amines

N-Heterocycles have been found in a large number of natural products, drug molecules, and bioactive compounds, and they thereby play a vital role in diverse research disciplines including drug discovery, organic synthesis, chemical biology, and material science. To this end, the development of new methods and strategies for the construction of N-heterocyclic frameworks is arguably one of the most dynamic and significant research areas in organic synthesis. One of these powerful approaches to the synthesis of N-heterocycles is to establish cyclization reactions based on the transformation of tertiary amines, which has emerged as an attractive research topic. In this Minireview, the significant achievements in the construction of N-heterocycles through cyclization of tertiary amines are highlighted and a comprehensive overview of the rational design, development, and application of these synthetic methods is presented.     

Addressing challenges in palladium-catalyzed cross-coupling reactions through ligand design

The development of palladium-catalyzed cross-coupling reactions has revolutionized the synthesis of organic molecules on both bench-top and industrial scales. While significant research effort has been directed toward evaluating how modifying various reaction parameters can influence the outcome of a given cross-coupling reaction, the design and implementation of novel ancillary ligand frameworks has played a particularly important role in advancing the state-of-the-art. This Review seeks to highlight notable examples from the recent chemical literature, in which newly developed ancillary ligands have enabled more challenging substrate transformations to be addressed with greater selectivity and/or under increasingly mild conditions. Throughout, the importance and subtlety of ligand effects in palladium-catalyzed cross-coupling reactions are described, in an effort to inspire further development and understanding within the field of ancillary ligand design.     

Reaction site mapping of xenobiotic biotransformations

Predictive metabolism methods can be used in drug discovery projects to enhance the understanding of structure-metabolism relationships. The present study uses data mining methods to exploit biotransformation data that have been recorded in the MDL Metabolite database. Reacting center fingerprints were derived from a comparison of substrates and their corresponding products listed in the database. This process yields two fingerprint databases: all atoms in all substrates and all reacting centers. The metabolic reaction data are then mined by submitting a new molecule and searching for fingerprint matches to every atom in the new molecule in both databases. An "occurrence ratio" is derived from the fingerprint matches between the submitted compound and the reacting center and substrate fingerprint databases. Normalization of the occurrence ratio within each submitted molecule enables the results of the search to be rank-ordered as a measure of the relative frequency of a reaction occurring at a specific site within the submitted molecule. Predictive performance that would allow this method to be used by drug discovery teams to generate useful hypotheses regarding structure metabolism relationships was observed.     

Use of multicomponent, domino, and other one-pot syntheses on solid phase: powerful tools for the generation of libraries of diverse and complex compounds

The availability of small organic molecules covering as much chemical space as possible is seen as the only means that guarantees potential modulation of the many biological targets that are ultimately being unveiled by genomics. Therefore diversity oriented organic synthesis is rapidly becoming one of the paradigms in the process of modern drug discovery. This has spurred research in those fields of chemical investigation that lead to the rapid assembly of not only molecular diversity, but also molecular complexity. As a consequence multi-component as well as domino or related reactions are witnessing a new spring. Coupling these one-pot processes with solid-phase synthesis offers new perspectives for the preparation of both primary and thematic libraries. The progresses recently made in this field that perfectly suits the needs of modern drug discovery are the subject of the present review.     

以亮氨酸合成为例刍议代谢中的生物化学相似性规律

生物体内纷杂多样的代谢过程存在诸多规律可循。本文以亮氨酸代谢为例提供一种教学的新模式。结合其他学科知识从基础代谢角度归纳总结生物代谢中化学原理的内在规律;同时,通过对比不同代谢路径中类似酶在进化上的相似性,发现自然界选择这种代谢方式的规律和原因。这种对比不同代谢路径异同的教学模式,可以帮助学生总结生物代谢不同路径之间的内在规律,加深对相关代谢过程的理解,同时也避免生物化学代谢过程杂乱无章、无迹可寻的错误印象,并增加学习过程的趣味性和思辨性。作为对基础生物化学和生物工程等相关课程中代谢过程教学的改革探索,希望对培养21世纪高素质人才有助。     

Evaporation‐induced Self‐assembly Process Controlled for Obtaining Highly Ordered Mesoporous Materials with Demanded Morphologies

A large number of periodic mesoporous materials have been reported using amphiphilic organic molecules with increasing development of synthetic methods for mesostructural, morphological, and compositional designs. The evaporation‐induced self‐assembly (ESIA) process to fabricate ordered mesoporous films is one of the most essential synthetic methods, which has extensively been applied for obtaining a wide variety of samples (e.g., films and monoliths, including powders). It contains complicated physical variations and chemical reactions, but has been simply explained by several research groups. However, a current, exact understanding of such complicated systems should be given with respect to all the variations and reactions. In this article, I have mainly surveyed the exact EISA process by considering the difference between simple and controlled EISA processes on the basis of my own experiments. I believe that the insights are consequently helpful for obtaining highly ordered mesoporous materials with demanded morphologies.