Integration of algebra and chemistry concepts with molecular descriptors: A problem-based-learning exercise

A problem-based learning experience integrating mathematical concepts of linear and abstract algebra for undergraduate chemistry students is presented. The pedagogical framework was focused on the conceptual understanding of the vector space, graph theory and matrix algebra as a tool to obtain chemical information. The students were capable to solve a problem of physicochemical properties prediction through the calculation of molecular descriptors of the TOMOCOMD (acronym for TOpological MOlecular COMputational Design) approach. A “scientific congress” was organized by students to expose the results of the research. This evaluation strategy stimulated the self- and co-evaluation. The proposed experience demonstrated an enhanced learning compared to the traditional model.En este trabajo se presenta una experiencia de de aprendizaje basado en problemas que integra conceptos matemáticos de algebra lineal y abstracta para estudiante de pregrado de química. El marco pedagógico se enfocó en el entendimiento conceptual de espacios vectoriales, teoría de grafos y álgebra matricial como una herramienta para obtener información química. Los estudiantes fueron capaces de resolver un problema de predicción de propiedades fisicoquímicas a través del cálculo de descriptores del esquema TOMOCOMD (siglas en inglés de TOpological MOlecular COMputational Design). Los estudiantes organizaron un “congreso científico” para exponer los resultados de la investigación. Esta evaluación estimuló el auto- y co-evaluación. La experiencia propuesta demostró un aprendizaje mayor al ser comparado con el modelo tradicional

Reviewing Ligand-Based Rational Drug Design: The Search for an ATP Synthase Inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs

Computational exploration of the chemical structure space of possible reverse tricarboxylic acid cycle constituents

The reverse tricarboxylic acid (rTCA) cycle has been explored from various standpoints as an idealized primordial metabolic cycle. Its simplicity and apparent ubiquity in diverse organisms across the tree of life have been used to argue for its antiquity and its optimality. In 2000 it was proposed that chemoinformatics approaches support some of these views. Specifically, defined queries of the Beilstein database showed that the molecules of the rTCA are heavily represented in such compound databases. We explore here the chemical structure space, e.g. the set of organic compounds which possesses some minimal set of defining characteristics, of the rTCA cycle's intermediates using an exhaustive structure generation method. The rTCA's chemical space as defined by the original criteria and explored by our method is some six to seven times larger than originally considered. Acknowledging that each assumption in what is a defining criterion making the rTCA cycle special limits possible generative outcomes, there are many unrealized compounds which fulfill these criteria. That these compounds are unrealized could be due to evolutionary frozen accidents or optimization, though this optimization may also be for systems-level reasons, e.g., the way the pathway and its elements interface with other aspects of metabolism

Similarity Methods in Chemoinformatics

promoting access to White Rose research paper

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

Encoding, Storing and Searching of Analytical Properties and Assigned Metabolite Structures

Informationen über Metabolite und andere kleine organische Moleküle sind von entscheidender Bedeutung in vielen verschiedenen Bereichen der Naturwissenschaften. Sie spielen z.B. eine entscheidende Rolle in metabolischen Netzwerken und das Wissen über ihre Eigenschaften, hilft komplexe biologische Prozesse und komplette biologische Systeme zu verstehen. Da in biologischen und chemischen Laboren täglich Daten anfallen, welche diese Moleküle beschreiben, existiert eine umfassende Datengrundlage, die sich kontinuierlich erweitert. Um Wissenschaftlern die Verarbeitung, den Austausch, die Archivierung und die Suche innerhalb dieser Informationen unter Erhaltung der semantischen Zusammenhänge zu ermöglichen, sind komplexe Softwaresysteme und Datenformate nötig. Das Ziel dieses Projektes bestand darin, Anwendungen und Algorithmen zu entwickeln, welche für die effiziente Kodierung, Sammlung, Normalisierung und Analyse molekularer Daten genutzt werden können. Diese sollen Wissenschaftler bei der Strukturaufklärung, der Dereplikation, der Analyse von molekularen Wechselwirkungen und bei der Veröffentlichung des so gewonnenen Wissens unterstützen. Da die direkte Beschreibung der Struktur und der Funktionsweise einer unbekannten Verbindung sehr schwierig und aufwändig ist, wird dies hauptsächlich indirekt, mit Hilfe beschreibender Eigenschaften erreicht. Diese werden dann zur Vorhersage struktureller und funktioneller Charakteristika genutzt. In diesem Zusammenhang wurden Programmmodule entwickelt, welche sowohl die Visualisierung von Struktur- und Spektroskopiedaten, die gegliederte Darstellung und Veränderung von Metadaten und Eigenschaften, als auch den Import und Export von verschiedenen Datenformaten erlauben. Diese wurden durch Methoden erweitert, welche es ermöglichen, die gewonnenen Informationen weitergehend zu analysieren und Struktur- und Spektroskopiedaten einander zuzuweisen. Außerdem wurde ein System zur strukturierten Archivierung und Verwaltung großer Mengen molekularer Daten und spektroskopischer Informationen, unter Beibehaltung der semantischen Zusammenhänge, sowohl im Dateisystem, als auch in Datenbanken, entwickelt. Um die verlustfreie Speicherung zu gewährleisten, wurde ein offenes und standardisiertes Datenformat definiert (CMLSpect). Dieses erweitert das existierende CML (Chemical Markup Language) Vokabular und erlaubt damit die einfache Handhabung von verknüpften Struktur- und Spektroskopiedaten. Die entwickelten Anwendungen wurden in das Bioclipse System für Bio- und Chemoinformatik eingebunden und bieten dem Nutzer damit eine hochqualitative Benutzeroberfläche und dem Entwickler eine leicht zu erweiternde modulare Programmarchitektur

Quantitative structure activity relationships in computer aided molecular design

The drug development process requires the complete evaluation and identification of the chosen substance as well as its properties. It involves extensive chemical examination to achieve the best therapeutic effects which demands huge expenditure both in terms of time and money. Computer aided molecular design (CAMD) allows the production of new substances with pre-decided properties. Additionally, in order to illustrate and determine the interrelationship between the chemical structure of a compound and its biological activity, Quantitative Structure Activity Relationship (QSAR) is applied by employing a mathematical model and arranging molecular descriptors. This paper presents review of CAMD and QSAR techniques. The most common chemometric techniques are also emphasized. CAMD and QSAR are considered to be extremely efficient instruments in molecular design and accelerate the initial steps of drug development process. Furthermore, they enhance the effectiveness and reduce the cost of newly developed drugs

Chemoinformatics approaches for new drugs discovery

Chemoinformatics uses computational methods and technologies to solve chemical problems. It works on molecular structures, their representations, properties and related data. The first and most important phase in this field is the translation of interconnected atomic systems into in-silico models, ensuring complete and correct chemical information transfer. In the last 20 years the chemical databases evolved from the state of molecular repositories to research tools for new drugs identification, while the modern high-throughput technologies allow for continuous chemical libraries size increase as highlighted by publicly available repository like PubChem [http://pubchem.ncbi.nlm.nih.gov/], ZINC [http://zinc.docking.org/], ChemSpider[http://www.chemspider. com/]. Chemical libraries fundamental requirements are molecular uniqueness, absence of ambiguity, chemical correctness (related to atoms, bonds, chemical orthography), standardized storage and registration formats. The aim of this work is the development of chemoinformatics tools and data for drug discovery process. The first part of the research project was focused on accessible commercial chemical space analysis; looking for molecular redundancy and in-silico models correctness in order to identify a unique and univocal molecular descriptor for chemical libraries indexing. This allows for the 0%-redundancy achievement on a 42 millions compounds library. The protocol was implemented as MMsDusty, a web based tool for molecular databases cleaning. The major protocol developed is MMsINC, a chemoinformatics platform based on a starting number of 4 millions non-redundant high-quality annotated and biomedically relevant chemical structures; the library is now being expanded up to 460 millions compounds. MMsINC is able to perform various types of queries, like substructure or similarity search and descriptors filtering. MMsINC is interfaced with PDB(Protein Data Bank)[http://www.rcsb.org/pdb/home/home.do] and related to approved drugs. The second developed protocol is called pepMMsMIMIC, a peptidomimetic screening tool based on multiconformational chemical libraries; the screening process uses pharmacophoric fingerprints similarity to identify small molecules able to geometrically and chemically mimic endogenous peptides or proteins. The last part of this project lead to the implementation of an optimized and exhaustive conformational space analysis protocol for small molecules libraries; this is crucial for high quality 3D molecular models prediction as requested in chemoinformatics applications. The torsional exploration was optimized in the range of most frequent dihedral angles seen in X-ray solved small molecules structures of CSD(Cambridge Structural Database); by appling this on a 89 millions structures library was generated a library of 2.6 x 10 exp 7 high quality conformers. Tools, protocols and platforms developed in this work allow for chemoinformatics analysis and screening on large size chemical libraries achieving high quality, correct and unique chemical data and in-silico model