X-ray crystallographic and theoretical studies of an anticonvulsant enaminone:methyl 4-(4'-bromophenyl)amino-6-methyl-2-oxocyclohex-3-en-1-oate

Objective: The aims of this study were to establish the structure of the potent anticonvulsant enaminone methyl 4-(4′-bromophenyl)amino-6-methyl-2- oxocyclohex-3-en-1-oate (E139), and to determine the energetically preferred conformation of the molecule, which is responsible for the biological activity. Materials and Methods: The structure of the molecule was determined by X-ray crystallography. Theoretical ab initio calculations with different basis sets were used to compare the energies of the different enantiomers and to other structurally related compounds. Results: The X-ray crystal structure revealed two independent molecules of E139, both with absolute configuration C11(S), C12(R), and their inverse. Ab initio calculations with the 6-31G, 3-21G and STO-3G basis sets confirmed that the C11(S), C12(R) enantiomer with both substituents equatorial had the lowest energy. Compared to relevant crystal structures, the geometry of the theoretical structures shows a longer C-N and shorter C=O distance with more cyclohexene ring puckering in the isolated molecule. Conclusion: Based on a pharmacophoric model it is suggested that the enaminone system HN-C=C-C=O and the 4-bromophenyl group in E139 are necessary to confer anticonvulsant property that could lead to the design of new and improved anticonvulsant agents. Copyright © 2003 S. Karger AG, Basel.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.