Stochastic Generator of Chemical Structure. 1. Application to the Structure Elucidation of Large Molecules

Faulon, Jean‐Loup

doi:10.1021/ci00021a031

Cited by 80 publications

(79 citation statements)

References 8 publications

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“…Over time, these methods have been improved by adding additional analytical techniques, such as 13 C NMR (Ali et al, 1990(Ali et al, , 2006Altgelt and Boduszynski, 1994;Cantor, 1978;Dickinson, 1980;Knight, 1967;Petrakis and Allen, 1987;Sato, 1997;Sato et al, 1998;Suzuki et al, 1982;Takegami et al, 1980), Size Exclusion Chromatography (SEC) (Al-Zaid et al, 1998;Gauthier et al, 2008), infrared spectroscopy (Montgomery and Boyd, 1959;Qian et al, 1984), functional groups analysis (Faulon, 1994) or pyrolysis analysis (Artok et al, 1999;Faulon et al, 1990;Kowalewski et al, 1996), which allows for more molecular input for the mixture and reduces the number of assumptions.…”

Section: Approach By Model Moleculementioning

confidence: 99%

A Review of Kinetic Modeling Methodologies for Complex Processes

Oliveira

Hudebine

Denis

et al. 2016

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

121

113

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-In this paper, kinetic modeling techniques for complex chemical processes are reviewed. After a brief historical overview of chemical kinetics, an overview is given of the theoretical background of kinetic modeling of elementary steps and of multistep reactions. Classic lumping techniques are introduced and analyzed. Two examples of lumped kinetic models (atmospheric gasoil hydrotreating and residue hydroprocessing) developed at IFP Energies nouvelles (IFPEN) are presented. The largest part of this review describes advanced kinetic modeling strategies, in which the molecular detail is retained, i.e. the reactions are represented between molecules or even subdivided into elementary steps. To be able to retain this molecular level throughout the kinetic model and the reactor simulations, several hurdles have to be cleared first: (i) the feedstock needs to be described in terms of molecules, (ii) large reaction networks need to be automatically generated, and (iii) a large number of rate equations with their rate parameters need to be derived. For these three obstacles, molecular reconstruction techniques, deterministic or stochastic network generation programs, and single-event micro-kinetics and/or linear free energy relationships have been applied at IFPEN, as illustrated by several examples of kinetic models for industrial refining processes.Résumé -Une revue de méthodes de modélisation cinétique pour des procédés complexesDans cet article, les techniques de modélisation cinétique des processus chimiques complexes sont examinées. Après un bref aperçu historique de la cinétique chimique, un aperçu des bases théoriques de la modélisation cinétique d'étapes élémentaires et de réactions globales est présenté. Les techniques classiques de regroupement (lumping) sont ensuite présentées et analysées. Deux exemples de modèles cinétiques regroupés (pour l'hydrotraitement de gazole atmosphérique et pour l'hydrotraitement de résidus) développés à IFP Energies nouvelles (IFPEN) sont présentés. La plus grande partie de cette revue décrit des stratégies avancées de modélisation cinétique, dans lesquelles le détail moléculaire est retenu : les réactions entre les molécules sont représentées ou même subdivisées en étapes élémentaires. Pour être en mesure de conserver ce niveau moléculaire à la fois dans le modèle cinétique et dans les simulations de réacteurs, plusieurs obstacles doivent d'abord être éliminés : (i) la charge doit être décrite en termes de molécules, (ii) les grands réseaux réactionnels doivent être générés automatiquement et (iii) un grand nombre d'équations de vitesse avec leurs paramètres de vitesse doit être dérivé. Pour ces trois obstacles, des techniques de reconstruction moléculaire, des programmes de génération de réseaux déterministes ou stochastiques, et des modèles microcinétiques basés sur des événements constitutifs (single events) et/ou des

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Section: Approach By Model Moleculementioning

confidence: 99%

A Review of Kinetic Modeling Methodologies for Complex Processes

Oliveira

Hudebine

Denis

et al. 2016

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

121

113

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“…The signature of a molecule is a vector whose components correspond to atomic signatures (Faulon, 1994). Each component of a molecular signature counts the number of occurrences of a particular atomic signature in the molecule.…”

Section: Molecular Signaturementioning

confidence: 99%

“…Our approach uses a graph-based representation of molecules known as signature (Faulon, 1994). This representation describes a molecular graph by decomposition into canonical subgraphs (Faulon et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

et al. 2007

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Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures.There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets. Availability and Contact: For questions, paper reprints, please contact Jean-Loup Faulon at jfaulon@sandia.gov. Additional information on the signature molecular descriptor and codes can be downloaded at:

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“…No entanto, tais estruturas são quimicamente corretas e compatíveis com os dados fornecidos, porém não são geradas todas as estruturas possíveis. Dentre os métodos estocásticos, pode-se citar o método de Faulon 33 e Algoritmos Genéticos (AG). O desenvolvimento de metodologias estocásticas para geração estrutural é recente e, até o momento, apenas o SENECA (Faulon + AG) e o GENIUS 34 (AG) as utilizam.…”

Section: Geração De Estruturasunclassified