Principles of the Generation of Constitutional and Configurational Isomers

Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200 °C were exhaustively generated.

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“…where p(z k ) is obtained as the result of parameter estimation with the EM algorithm as Equation 10.…”

Section: Inverse Analysis Using a Linear Gaussian Modelmentioning

confidence: 99%

“…The structure generation system MOLGEN [10] is one of the fastest and most sophisticated programs that applies the concept of homomorphism. MOLGEN can efficiently generate a large number of virtual structures under some primitive constraint such as molecular formula, the number of double bonds, and so on.…”

Section: Introductionmentioning

confidence: 99%

Exhaustive Structure Generation for Inverse‐QSPR/QSAR

Miyao

Arakawa

Funatsu

2010

Molecular Informatics

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“…SAG "identification parameters" are specified as follows: active major value (AMV), total active value (TAV), inactive (8,8); (9,9);(10,10); (11,11); (12,12); (13,13); (14,14); (15,15); (16,16); (17,17); (18,18); (19,19); (20,20); (21,21); (22,23); (24,24); (25,25); (26,26); (27,27); (28,28) (10,17); (11,20); (12,19); (13,18); (14,15); …”

Section: Stereoisomer Generationmentioning

confidence: 99%

Algorithm for Exhaustive and Nonredundant Organic Stereoisomer Generation

Contreras

Álvarez

Guajardo

et al. 2006

J. Chem. Inf. Model.

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Generation of organic stereoisomers with R/S, Z/E, and/or M/P configurations that may contain heteroatoms, multiple bonds, and any kind of cycle (isolated, spiro, condensed, and nested) is described. Inputs for processing are molecular structures in a N_tuple format resident on an automatic (canonical) or manual (non canonical) generated file which are processed by doing internal molecular graph construction, a weighted bipartite tree construction for all atoms and bonds to detect stereocenters, and symmetrical atom groups (SAG) with some specific SAG parameters that constitute a novel way for redundancy elimination of meso structures. Finally, determination of ligand CIP priorities allows for writing the output N_tuples with stereoisomer description. Several examples showing application of this methology to a wide number of structures are also presented.

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“…Moreover, not only the chemical pathways, but the conformational space of the participating molecules and radicals has to be explored as well. There is a large literature on strategies to explore chemical pathways (for example [5][6][7][8][9][10][11]), but there are no readily available programs that can be used to perform this task in a way that is most useful and efficient to the gas-phase chemical kinetics community. The goal of this project was to create a computer code that provides an efficient way to explore chemical pathways automatically for reactions that are relevant in gas-phase chemical problems.…”

Section: Introductionmentioning

confidence: 99%

Automated exploration of the mechanism of elementary reactions

Najm

Zádor

2012

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Optimization of new transportation fuels and engine technologies requires the characterization of the combustion chemistry of a wide range of fuel classes. Theoretical studies of elementary reactions -the building blocks of complex reaction mechanisms -are essential to accurately predict important combustion processes such as autoignition of biofuels. The current bottleneck for these calculations is a user-intensive exploration of the underlying potential energy surface (PES), which relies on the "chemical intuition" of the scientist to propose initial guesses for the relevant chemical configurations. For newly emerging fuels, this approach cripples the rate of progress because of the system size and complexity. The KinBot program package aims to accelerate the detailed chemical kinetic description of combustion, and enables large-scale systematic studies on the sub-mechanism level.

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Principles of the Generation of Constitutional and Configurational Isomers

Cited by 54 publications

References 26 publications

Exhaustive Structure Generation for Inverse‐QSPR/QSAR

Exhaustive Structure Generation for Inverse‐QSPR/QSAR

Algorithm for Exhaustive and Nonredundant Organic Stereoisomer Generation

Automated exploration of the mechanism of elementary reactions

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