A new development of the oasis computer system for modeling molecular properties

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.

Probabilistic assessment of biodegradability based on metabolic pathways: catabol system

A novel mechanistic modeling approach has been developed that assesses chemical biodegradability in a quantitative manner. It is an expert system predicting biotransformation pathway working together with a probabilistic model that calculates probabilities of the individual transformations. The expert system contains a library of hierarchically ordered individual transformations and matching substructure engine. The hierarchy in the expert system was set according to the descending order of the individual transformation probabilities. The integrated principal catabolic steps are derived from set of metabolic pathways predicted for each chemical from the training set and encompass more than one real biodegradation step to improve the speed of predictions. In the current work, we modeled O2 yield during OECD 302 C (MITI I) test. MITI-I database of 532 chemicals was used as a training set. To make biodegradability predictions, the model only needs structure of a chemical. The output is given as percentage of theoretical biological oxygen demand (BOD). The model allows for identifying potentially persistent catabolic intermediates and their molar amounts. The data in the training set agreed well with the calculated BODs (r2 = 0.90) in the entire range i.e. a good fit was observed for readily, intermediate and difficult to degrade chemicals. After introducing 60% ThOD as a cut off value the model predicted correctly 98% ready biodegradable structures and 96% not ready biodegradable structures. Crossvalidation by four times leaving 25% of data resulted in Q2 = 0.88 between observed and predicted values. Presented approach and obtained results were used to develop computer software for biodegradability prediction CATABOL.

QSAR for acetylcholinesterase inhibition and toxicity of two classes of phosphoramidothioates

Methamidophos (Met) is a weak inhibitor of housefly head AChE but at the same time it is highly toxic to the common housefly. The lethality of Met is believed to be due to AChE inhibition. An extensive QSAR study may help in determining the mode of action of Met in vivo and in vitro and provide a rational for its high insecticidal toxicity. Acephate (Ace), like Met, is a poor inhibitor of AChE in vitro and has a comparable to Met insect toxicity in vivo. Contrary to Met, though, Ace has much lower mammalian toxicity. Understanding the structural properties which make insecticides toxic to insects but not to mammals is of great importance, since mammals (including humans) are inadvertently exposed to these compounds. Our results were consistent with the model in which both the in vitro and in vivo toxicity of Met depends on the inhibition of the active center of AChE by the unchanged Met. An optimal susceptibility to hydrolysis is needed for Met and its analogs to have high insecticidal activity since in order to phosphorylate AChE they need to be hydrolyzed and at the same time their stability is of great importance in vivo for accumulating at the site of action. The insecticidal activity of Ace analogs may be due to direct interaction with the active center of the AChE. The mammalian toxicity of Ace analogs may be due to interaction with an 'allosteric' reaction center in the AChE.

Evaluation in quantitative structure--property relationship models of structural descriptors derived from information-theory operators

During the search for new structural descriptors we have defined the information-theory operators U(M), V(M), X(M), and Y(M), that are computed from atomic invariants and measure the information content of the elements of molecular matrices. Structural descriptors computed with these four information-theory operators are used to develop structure-property models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. The information-theory operators were applied to six molecular matrices, namely, the distance D, the reciprocal distance RD, the distance-path Dp, the reciprocal distance-path RDp, the path Szeged Sz(p), and the reciprocal path Szeged RSz(p) matrices. In combination with other topological indices, the information-theory indices offer good structure-property models for all six alkane properties investigated in this study.

The use of computer based structure-activity relationships in the risk assessment of industrial chemicals

The concept of a two-step approach toward the assessment of toxicity endpoints for a chemical is proposed. The first step involves the selection of chemical analogues for which toxicity data is available in a noncongeneric database. The next step is the derivation of a Quantitative Structure-Activity Relationship (QSAR) for the chemical domain, predetermined by the selection rules. The software tools needed for the computer implementation of such an approach are summarized. By making use of them, we have derived aquatic toxicity QSARs, of which two are given as example. The latter pertain to chemicals that have been automatically extracted from noncongeneric databases, after defining the substructure recognition rules implied by the putative mechanism of toxicity.

Update on computer-aided drug design

Recent advances in computational methods for drug design include developments in quantitative structure-activity relationship approaches as well as novel structure-based strategies. Many new protein structures of pharmaceutical interest have been solved, a number of which contain a bound inhibitor. Continued progress has been reported in algorithms for de novo design, ligand docking, and scoring of protein-ligand binding energy. Meanwhile, several drugs that were designed by intensive use of computational methods are advancing through clinical trials.