Estimation of aqueous solubility of chemical compounds using E-state indices.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.

Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds.

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.

Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet.

This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.

[Kinetic characteristics and enantioselective action of penicillinase in the hydrolysis reaction of N-phenylacetyl derivatives of 1-aminoethylphosphonic acid and its esters]. / Kineticheskie zakonomernosti i énantioselektivnost' deistviia penitsillinazy v reaktsii gidroliza N-fenilatsetil'nykh proizvodnykh 1-aminoétilfosfonovoi kisloty i ee éfirov.

Penicillin acylase from E. coli (EC 3.5.1.11) was found to hydrolyze N-phenylacetylated 1-aminoethylphosphonic acid and its esters. The enzyme preferentially converts the R-form of the substrates: the ratios of the bimolecular rate constants of penicillin acylasecatalyzed hydrolysis of R- and S-forms of 1-(N-phenylacetamino)-ethylphosphonic acid and its dimethyl- and diisopropyl-esters are 58000, 2300, 1800; these derivatives were shown to have the greatest values of the catalytic constants for enzymatic hydrolysis of all known substrates for penicillin acylase: 237, 148 and 134 s-1; the corresponding Km values are 3.7 10(-5), 6.8 10(-4) and 6.2 10(-4) M at pH 7.0. The kinetics of enzymatic hydrolysis of 1-(N-phenylacetamino)-ethylphosphonic acid was investigated up to high degrees of conversion. The inhibition of penicillin acylase by high concentrations of the R-form of the substrate (with substrate inhibition constant of 0.07 M) and competitive inhibition by the reaction product, phenylacetic acid (Ki = 3.5 10(-5) M), was observed.