Cheminformatic approaches have been employed to optimize the bis-coumarin scaffold identified by Onywera et al. (2012) as a potential hit against the protease HIV-1 protein. The Open Babel library of commands was used to access functions that were incorporated into a markov chain recursive program that generated 17750 analogues of the bis-coumarin scaffold. The Morita-Baylis-Hillman accessible heterocycles were used to introduce structural diversity within the virtual library. In silico high through-put virtual screening using AutoDock Vina was used to rapidly screen the virtual library ligand set against 61 protease models built by Onywera et al. (2012). CheS-Mapper computed a principle component analysis of the compounds based on 13 selected chemical descriptors. The compounds were plotted against the principle component analysis within a 3 dimensional chemical space in order to inspect the diversity of the virtual library. The physicochemical properties and binding affinities were used to identify the top 3 performing ligands. ACPYPE was used to inspect the constitutional properties and eliminated virtual compounds that possessed open valences. Chromene based ligand 805 and ligand 6610 were selected as the lead candidates from the high-throughput virtual screening procedure we employed. Molecular dynamic simulations of the lead candidates performed for 5 ns allowed the stability of the ligand protein complexes with protease model 305152. The free energy of binding of the leads with protease model 305152 was computed over the first 50 ps of simulation using the molecular mechanics Poisson-Boltzmann method. Analysis structural features and energy profiles from molecular dynamic simulations of the protein–ligand complexes indicated that although ligand 805 had a weaker binding affinity in terms of docking, it outperformed ligand 6610 in terms of complex stability and free energy of binding. Medicinal chemistry approaches will be used to optimize the lead candidates before their analogues will be synthesized and assayed for in vivo protease activity.

A doença de Chagas representa um grave problema de saúde em regiões endêmicas que vão desde o sul dos Estados Unidos até a Argentina. O protozoário tripanossomatídeo Trypanosoma cruzi é o agente causador dessa devastadora doença, que afeta milhões de pessoas. Existem em torno de 10 milhões de indivíduos contaminados e pelo menos 25 milhões de pessoas vivem em locais riscos de infecção. Os dois medicamentos, o nifurtimox e o benzonidazol, apresentam sérios efeitos colaterais além de se mostrarem ineficazes na fase crônica da doença. Esse triste perfil, felizmente, tem se alterado com recentes avanços que levaram o ravuconazol, pozaconazol e K11777 para estudos em fase clínica. Com base em seu papel fundamental no ciclo do T. cruzi, a sexta enzima da via glicolítica, a gliceraldeído 3-fosfato desidrogenase (GAPDH) vem sendo considerada um alvo promissor para a descoberta e o desenvolvimento de novos agentes quimioterápicos para o tratamento da doença de Chagas. É amplamente conhecida a importância do planejamento de compostos tanto por método baseado na estrutura do alvo quanto do ligante. A docagem molecular foi usada para a seleção inicial dos compostos para o teste biocalorimétrico, e a partir dessa estratégia foi possível, de 25 compostos. Os parâmetros cinéticos da catálise da TcGAPDH foram determinados (KM = 10,51 ± 0,91 µM, Vmax = 4,18 ± 0,09 x 10-4 mM s-1 e kcat = 85,88 ± 3,22 s-1). Os experimentos cinéticos por ITC possibilitou na identificação de cinco compostos bioativos, sendo três com constante de inibição abaixo de 100 µM (13,21 ± 0,88, 35,00 ± 1,70 e 78,45 ± 2,69 µM). Processos de simulação de dinâmica molecular foram aplicados para a predição do modo de interação dos três compostos com Ki app menores que 100 µM. / Chagas disease is a serious health problem in endemic regions ranging from the southern the United States to Argentina. The protozoan Trypanosoma cruzi is the causative agent of this devastating disease that affects millions of people. Exist about 10 million people infected and at least 25 million people live in risk of local infection. There are only two drugs used to treat Chagas disease during acute phase and it show harmful side effects. This gloomy outlook has changed due to major advances in research of anti-trypanosomatid agents; an example is posaconazole, ravuconazole and K11777 both, which currently is in clinical phase. A promising target that is receiving considerable attention is the enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH, EC 1.2.1.12) a key protein in the glycolytic pathway of trypanosomatids. SBVS methods were used for the selection of 25 compounds and these were assayed against GAPDH using Isothermal Titration Calorimetry. The kinetic parameters of catalysis were determined TcGAPDH (KM = 10.91 ± 0.91 µM, Vmax = 4.18 ± 0.09 x 10-4 mM s-1 and kcat = 85.88 ± 3.22 s-1). The kinetic experiments by ITC allowed the identification of five bioactive compounds, three with inhibition constant below 100 µM. Simulation process of molecular dynamics were applied to predict the mode of interaction for the three compounds with Kiapp less than 100 µM.

No description available.

This PhD project is on the algorithmic aspects of synthesis planning and mass spectrometry; two separate chemical problems concerning the understanding of molecules and how these behave. Part I: In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it. We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs, not necessarily using a pre-defined bond set. For this, individual synthesis plans are modeled as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, application of a know polynomial time algorithm to find the K shortest hyperpaths yields the K best synthesis plans for a given target molecule. To this end, classical quality measures are discussed. Having K good plans to choose from has several benefits: It makes the synthesis planning process much more robust when in later stages adding further chemical details, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates. An empirical study of our method illustrates the limitations of what a chemist can expect is feasible to compute, as well as the practical value of our method for cases where yield estimates are imprecise or unknown. To illustrate the realism of the approach, synthesis plans from our abstraction level are compared with detailed chemical synthesis plans from the literature. For this, a synthesis plan for Wieland-Miescher ketone and a synthesis plan for lysergic acid are used. In addition, equivalence of our structural definition of a hyperpath and two definitions from the hypergraph literature is shown. Part II: Mass spectrometry is an analytic technique for characterizing molecules and molecular mixtures, by gaining knowledge of their structure and composition from the way they fragment. In a mass spectrometer, molecules or molecular mixtures are ionized and fragmented, and the abundances of the different fragment masses are measured, resulting in so-called mass spectra. We suggest a new road map improving the current state-of-the art in computational methods for mass spectrometry. The main focus is on increasing the chemical realism of the modeling of the fragmentation process. Two core ingredients for this are i) describing the individual fragmentation reactions via graph transformation rules and ii) expressing the dynamics of the system via reaction rates and quasi-equilibrium theory. Graph transformation rules are used both for specifying the possible core fragmentation reactions, and for characterizing the reaction sites when learning values for the rates. We believe that this model describes chemical mechanisms more accurately than previous ones, and that this can lead to both better spectrum prediction and more explanatory power. Our modeling of system dynamics also allows better separation of instrument dependent and instrument independent parameters of the model.

The aim of the dissertation to study small organic solute rejection in organic solvent nanofiltration using cheminformatics and machine learning. Chemically diverse datasets were curated using a novel medium high-throughput methodology and literature data extraction. These datasets contained thousands of datapoints of small organic solute rejections and process parameters. Different chemical fingerprinting has been used for feature generation, such as molecular descriptors, Morgan fingerprints, and latent-vector representation. These features were used to train different machine learning models, such as graph neural networks, partial least squares regression, and boosting tree algorithms. The obtained models were used to predict small organic solute rejection for nanofiltration related applications. Correlation between rejection and the molecular descriptors have been studied to deepen the understanding of transport and rejection through polymeric membranes. Explainable artificial intelligence concept was used to study the effect of solute, solvent and membrane structure on solute rejection. The conclusion of the dissertation highlights the importance of the chemical structure effect in nanofiltration and provides a future perspective on data-driven approached for nanofiltration and organic solvent nanofiltration.

De nombreux produits chimiques organiques usuels sont susceptibles de devenir instables lorsqu'ils sont stockés de manière inadéquate ou durant de longues périodes au contact de l'air. Ils peuvent réagir avec l'oxygène moléculaire, y compris { température ambiante, par un processus d'autoxydation (peroxydation), qui se déroule selon un mécanisme radicalaire de réactions en chaine. Ce processus mène { la formation d'espèces chimiques peroxydées, thermodynamiquement instables et reconnus responsables de nombreux accidents de laboratoire. La présence d'espèces radicalaires, de plusieurs chemins réactionnels et le fait que les produits de réaction soient souvent des mélanges de composés, rendent la caractérisation expérimentale du processus de peroxydation très difficile. L'objectif de cette thèse, qui s'intègre dans le cadre du programme de recherche RIPER (étude des RIsques liés { la PERoxydation des produits chimiques) de l'INERIS, a été l'étude, par des méthodes de modélisation moléculaire, du processus d'autoxydation d'une famille d'espèces facilement peroxydables, les éthers, afin d'identifier les risques accidentels liés { leur utilisation, notamment dans des conditions normales de stockage. Dans ce but, une étude mécanistique détaillée a été menée sur l'oxydation de l'éther diéthylique (DEE) en utilisant une double approche de modélisation moléculaire (DFT) et cinétique. Le mécanisme réactionnel identifié pour le processus ainsi que le modèle cinétique détaillé développé, démontrent que, la compétition entre la voie réactionnelle de décomposition du radical alkyle issu de l'étape d'initiation et celle d'isomérisation des radicaux peroxyde est au coeur du processus d'oxydation du DEE est et que, la prépondérance d'un chemin par rapport { l'autre, dépend de la concentration d'oxygène dissous dans le solvant. Ces deux approches complémentaires indiquent aussi que le risque accidentel lié à cet éther est dû { l'accumulation de certaines espèces peroxydées produites (hydroperoxydes notamment). Les voies réactionnelles principales caractérisées pour le processus d'oxydation du DEE ont ensuite été étudiées pour treize autres éthers aliphatiques. Les résultats ont mis en évidence un comportement commun des éthers vis-à-vis de leur oxydation et cela a permis d'évaluer les énergies en jeu dans les étapes clefs du processus pour toutes les espèces considérées en s'affranchissant d'une étude détaillée. Enfin, le mécanisme d'inhibition de l'oxydation du DEE par ajout d'espèces chimiques antioxydantes a également été examiné par modélisation moléculaire. Les résultats préliminaires démontrent que les antioxydants phénoliques sont les plus efficaces pour l'inhibition du processus et confirment que le BHT (hydroxytoluène butylé), qui est déj{ l'antioxydant le plus utilisé pour la stabilisation du DEE, est l'inhibiteur le plus performant parmi les 12 étudiés.

New high throughput experimental techniques have turned the life sciences into a data-intensive field. Scientists are faced with new types of problems, such as managing voluminous sources of information, integrating heterogeneous data, and applying the proper analysis algorithms; all to end up with reliable conclusions. These challenges call for an infrastructure of algorithms and technologies to supply researchers with the tools and methods necessary to maximize the usefulness of the data. eScience has emerged as a promising technology to take on these challenges, and denotes integrated science carried out in highly distributed network environments, or science that makes use of large data sets and requires high performance computing resources. In this thesis I present standards, exchange formats, algorithms, and software implementations for empowering researchers in the life sciences with the tools of eScience. The work is centered around Bioclipse - an extensible workbench developed in the frame of this thesis - which provides users with instruments for carrying out integrated research and where technical details are hidden under simple graphical interfaces. Bioclipse is a Rich Client that takes full advantage of the many offerings of eScience, such as networked databases and online services. The benefits of mixing local and remote software in a unifying platform are demonstrated with an integrated approach for predicting metabolic sites in chemical structures. To overcome the limitations of the commonly used technologies for interacting with networked services, I also present a new technology using the XMPP protocol. This enables service discovery and asynchronous communication between the client and server, which is ideal for long-running analyses. To maximize the usefulness of the available data there is a need for standards, ontologies, and exchange formats, in order to define what information should be captured and how it should be structured and exchanged. A novel format for exchanging QSAR data sets in a fully interoperable and reproducible form is presented, together with an implementation in Bioclipse that takes advantage of eScience components during the setup process. Bioclipse has been well received by the scientific community, attracted a large group of international users and developers, and has been awarded three international prizes for its innovative character. With continued development, the project has a good chance of becoming an important component in a sustainable infrastructure for the life sciences.

La sélection de sous-ensembles de molécules diverses est un enjeu très important de la recherche pharmaceutique. En effet, de la qualité de cette sélection, dépendra la découverte efficace d'un médicament. De nombreuses méthodes existent pour répondre à cette demande. Certaines sont basées sur la création de groupes de molécules, d'autres sur le principe de dissimilarité inter-moléculaire. Nous proposons dans ce travail, une nouvelle technique à la croisée de ces méthodes, qui permet d'obtenir des sous-ensembles à la fois divers dans l'espace et représentatifs de l'ensemble initial duquel ils sont extraits. Pour créer cette méthode de sélection, nous avons tout d'abord défini et formalisé mathématiquement un critère de diversité, puis nous nous sommes appuyés sur des heuristiques connues en apprentissage artificiel pour concevoir l'algorithme. Celui-ci a été comparé à d'autres types de sélections de diversité couramment utilisées en chémoinformatique telles que les k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. La formalisation du critère de diversité nous a enfin permis de proposer un nouveau critère d'évaluation de la qualité des sélections. La méthode et le critère présentés dans ce travail donnent des échantillons divers et représentatifs d'un espace chimique.

The chemicals produced by biological systems, whether proteins, peptides, or terpenes, will always provide an intriguing topic for researchers. Invisibly controlling every aspect of nature, these molecules are responsible for life, evolution, and death. Specifically, here is described the secondary metabolites produced by Antarctic marine organisms as well as others, and how they are used to defend or attract other animals while potentially providing health benefits to mankind. This is done through collection, extraction, and separation of individual specimens. The respective mixtures of compounds after isolation are then analyzed via spectroscopic methods such as nuclear magnetic resonance spectroscopy, mass spectrometry, and X-ray crystallography. Once identified, these compounds are tested in biological assays to provide a hypothesis for their use in nature or evidence that there may be a use for them in medicine. For this thesis, the Antarctic organisms described are an alga, Pocamium cartilagineum, an amphipod, Paradexamine fissicauda, a sponge, Dendrilla membranosa, and one undescribed and two known deep sea coral species, Briareopsis aegeon and Plumarella delicatissima. Beyond these specific specimens, their chemistry as well as natural products from other origins were combined to create a diverse compound library for biological screening against human pathogens. This was done using computational modeling and statistical analysis of the compound library and its comparison to other known chemical libraries. The diversity and impact of these molecules are assessed.

Description Logics form a family of powerful ontology languages widely used by academics and industry experts to capture and intelligently manage knowledge about the world. A key advantage of Description Logics is their amenability to automated reasoning that enables the deduction of knowledge that has not been explicitly stated. However, in order to ensure decidability of automated reasoning algorithms, suitable restrictions are usually enforced on the shape of structures that are expressible using Description Logics. As a consequence, Description Logics fall short of expressive power when it comes to representing cyclic structures, which abound in life sciences and other disciplines. The objective of this thesis is to explore ontology languages that are better suited for the representation of structured objects. It is suggested that an alternative approach which relies on nonmonotonic existential rules can provide a promising candidate for modelling such domains. To this end, we have built a comprehensive theoretical and practical framework for the representation of structured entities along with a surface syntax designed to allow the creation of ontological descriptions in an intuitive way. Our formalism is based on nonmonotonic existential rules and exhibits a favourable balance between expressive power and computational as well as empirical tractability. In order to ensure decidability of reasoning, we introduce a number of acyclicity criteria that strictly generalise many of the existing ones. We also present a novel stratification condition that properly extends `classical' stratification and allows for capturing both definitional and conditional aspects of complex structures. The applicability of our formalism is supported by a prototypical implementation, which is based on an off-the-shelf answer set solver and is tested over a realistic knowledge base. Our experimental results demonstrate improvement of up to three orders of magnitude in comparison with previous evaluation efforts and also expose numerous modelling errors of a manually curated biochemical knowledge base. Overall, we believe that our work lays the practical and theoretical foundations of an ontology language that is well-suited for the representation of structured objects. From a modelling point of view, our approach could stimulate the adoption of a different and expressive reasoning paradigm for which robustly engineered mature reasoners are available; it could thus pave the way for the representation of a broader spectrum of knowledge. At the same time, our theoretical contributions reveal useful insights into logic-based knowledge representation and reasoning. Therefore, our results should be of value to ontology engineers and knowledge representation researchers alike.