Prediction of tissue: air partition coefficients--theoretical vs. experimental methods.

Predictive QSAR models for rat and human tissue : air partition coefficients, namely blood : air, fat : air, brain : air, liver : air, muscle : air, and kidney : air were developed utilizing experimentally determined partition coefficients for 131 chemicals obtained from the literature and molecular descriptors based solely on chemical structure. The descriptors were partitioned into four hierarchical classes, including topostructural, topochemical, 3-dimensional, and ab initio quantum chemical. Three types of regression methodologies--ridge regression, principal components regression, and partial least squares regression--were used comparatively in the development of the structure-based models. In addition to the structure-based models, ordinary least squares regression was used to develop comparative models based on experimentally determined properties including saline : air and olive oil : air partition coefficients. The results of the study indicate that many of the structure-based models are comparable or superior to their respective property-based models. This is an important result considering that structural descriptors can be calculated quickly and inexpensively for both existing chemicals and those not yet synthesized. It was also found that ridge regression outperformed principal components regression and partial least squares regression, with respect to the structure-based models, and that generally the topochemical descriptors alone produced models of good predictive ability.

Optimal neighbor selection in molecular similarity: comparison of arbitrary versus tailored prediction spaces.

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs (APs), topological indices (TIs), and principal components (PCs) derived from topological indices. Tailored QMSA models have been developed from TIs selected through ridge regression. K-nearest neighbor (kNN) based estimation has been applied to all of the methods to estimate normal vapor pressure (p(vap)) and water solubility (sol) for a set of 194 chemicals. Results show that the tailored QMSA methods are superior to arbitrary similarity methods in estimating both of these properties for the given set of chemicals.

Novel matrix invariants for characterization of changes of proteomics maps.

Previous studies on mathematical characterization of proteomics maps by sets of map invariants were based on the construction of a set of distance-related matrices obtained by matrix multiplication of a single matrix by itself. Here we consider an alternative characterization of proteomics maps based on a set of matrices characterizing local features of an embedded zigzag curve over the map. It is shown that novel invariants can well characterize proteomics maps. Advantages of the novel approach are discussed.

Quantitative molecular similarity analysis (QMSA) methods for property estimation: a comparison of property-based, arbitrary, and tailored similarity spaces.

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs, topological indices, and physicochemical properties. Tailored QMSA models have been developed using a selected number of TIs chosen by ridge regression. The methods have been applied to the K-nearest neighbor based estimation of log P of two sets of chemicals. Results show that the property-based and tailored QMSA methods are superior to the arbitrary similarity methods in estimating log P of both sets of chemicals

Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: a hierarchical QSAR approach.

Due to the lack of experimental data, there has been increasing use of theoretical structural descriptors in the hazard assessment of chemicals. We have used a hierarchical approach to develop class-specific quantitative structure-activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines. The hierarchical approach begins with the simplest molecular descriptors, the topostructural, which encode limited chemical information. The complexity is then increased, adding topochemical, geometric, and finally quantum chemical parameters. We have also added log P to the set of independent variables. The results indicate that the topological parameters, i.e., the topostructural and topochemical indices, explain the majority of the variance, and that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models.

Molecular similarity based estimation of properties: a comparison of structure spaces and property spaces.

Molecular similarity methods have emerged as powerful tools in analog selection, chemical classification based on toxic modes of action, and property estimation. The basic assumption of structure-activity relationships (SAR) is that similar structures usually have similar properties. Therefore, similarity methods can be used for the selection of analogs and estimation of properties of chemicals from their structural analogs in property spaces. Each similarity method is user defined. Its efficacy depends on the set of descriptors used to define the intermolecular similarity of chemicals as well as on the mathematical function used to quantify similarity. Also, similarity methods can be based on experimental data or computed molecular descriptors. We have carried out a comparative study of similarity spaces derived from experimental data vis-a-vis computed structural parameters for two sets of chemicals: (a) a diverse set of 76 chemicals derived from the TSCA Inventory and (b) the 166 structurally distinct constituents of JP-8 identified by GC/MS. Property spaces for these two sets of chemicals were created using experimental and calculated physicochemical properties. Atom pairs (APs) and topological indices calculated by POLLY v2.3 were used to create theoretical structure spaces. These spaces were used for the KNN-based estimation of properties with K=1--10, 15, 20, 25. The results will be presented with a comparative analysis of the effectiveness of property spaces and structure spaces in analog selection and property estimation.

Molecular similarity-based estimation of properties: a comparison of three structure spaces.

Similarity, like beauty, is an intuitive concept based on personal perception and bias. In the realm of molecular similarity, each method is user defined based on the features deemed important. A method's efficacy depends on the set of descriptors used to define the intermolecular similarity of chemicals and on the mathematical function used to quantify similarity. Quantitative molecular similarity analysis (QMSA) methods, based on experimental data or computed molecular descriptors, have emerged as powerful tools for analog selection and property estimation. We have carried out a comparative study of similarity spaces derived from atom pairs and a large set of topological indices for two diverse sets of chemicals: (a) a set of 469 chemicals with vapor pressure data from the TSCA inventory, and (b) a set of 213 chemicals with lipophilicity data from the STARLIST inventory. These spaces were used for the KNN-based estimation of properties (K = 1-10, 15, 20, 25). The results for the QMSA models developed in this paper are also compared with model estimates derived from hierarchical QSARs.

Use of statistical and neural net approaches in predicting toxicity of chemicals.

Hierarchical quantitative structure-activity relationships (H-QSAR) have been developed as a new approach in constructing models for estimating physicochemical, biomedicinal, and toxicological properties of interest. This approach uses increasingly more complex molecular descriptors in a graduated approach to model building. In this study, statistical and neural network methods have been applied to the development of H-QSAR models for estimating the acute aquatic toxicity (LC50) of 69 benzene derivatives to Pimephales promelas (fathead minnow). Topostructural, topochemical, geometrical, and quantum chemical indices were used as the four levels of the hierarchical method. It is clear from both the statistical and neural network models that topostructural indices alone cannot adequately model this set of congeneric chemicals. Not surprisingly, topochemical indices greatly increase the predictive power of both statistical and neural network models. Quantum chemical indices also add significantly to the modeling of this set of acute aquatic toxicity data.

A comparative QSAR study of benzamidines complement-inhibitory activity and benzene derivatives acute toxicity.

A novel QSAR study of benzamidines complement-inhibitory activity and benzene derivatives acute toxicity is reported and a new efficient method for selecting descriptors is used. Complement-inhibitory activity QSAR models of benzamidines contain from one to five descriptors. The best, according to fitted and cross-validated statistical parameters, is shown to be the five-descriptor model. Models with a higher number of indices did not improve over the five-descriptor model. The benzene derivatives structure-toxicity models involve up to seven linear descriptors. Multiregression models, containing up to ten nonlinear descriptors, are also reported for the sake of comparison with previously obtained additivity models. Comparison with benzamidine complement-inhibitory activity models and with benzene derivatives toxicity models from the literature favors our novel approach.

Prediction of the dermal penetration of polycyclic aromatic hydrocarbons (PAHs): a hierarchical QSAR approach.

Attempts were made to develop hierarchical quantitative structure-activity relationship (QSAR) models for the dermal penetration of polycyclic aromatic hydrocarbons (PAHs) using four classes of theoretical structural parameters; viz., topostructural, topochemical, geometric, and quantum chemical descriptors; and physicochemical properties such as molecular weight (MW) and lipophilicity (log P--octanol/water). The results show that topostructural, topochemical, and geometric descriptors and molecular weight are equally effective in predicting the dermal penetration of PAHs. Quantum chemical parameters did not make any improvements in the predictive power of the QSAR models.

Prediction of complement-inhibitory activity of benzamidines using topological and geometric parameters.

A hierarchical approach to quantitative structure-activity relationship (QSAR) modeling has been used to estimate the complement-inhibitory potency of 105 benzamidines. This hierarchical approach uses topostructural, topochemical, and geometric parameters in a stepwise fashion to build increasingly more complex models. The results show that topostructural indices alone, specifically I(D), predict inhibitory potency reasonably well. The addition of topochemical and geometrical parameters to the set of descriptors provides only marginal improvement in predictive power. However, when taken alone, the geometric parameter (3D)W provides a more stable model than the topostructural one.

Assessment of the mutagenicity of aromatic amines from theoretical structural parameters: a hierarchical approach.

Abstract A hierarchical approach has been used in this paper in predicting the mutagenicity/non-mutagenicity of a set of 127 chemicals from their molecular descriptors. The set of descriptors consisted of topostructural and topochemical parameters, experimental properties like log P, and quantum chemical indices calculated using a semi-empirical method. The results show that a combination of topostructural and topochemical molecular descriptors explain most of the variance in the experimental data. The addition of physical properties or quantum chemical parameters did not make any significant improvement in the predictive power of the models.

Predicting acute toxicity (LC50) of benzene derivatives using theoretical molecular descriptors: a hierarchical QSAR approach.

Four classes of theoretical structural parameters, viz., topostructural, topochemical, geometrical and quantum chemical descriptors, have been used in the development of quantitative structure-activity relationship (QSAR) models for a set of sixty-nine benzene derivatives. None of the individual classes of parameters was very effective in predicting toxicity. A hierarchical approach was followed in using a combination of the four classes of indices in QSAR model development. The results show that the hierarchical QSAR approach using the algorithmically derived molecular descriptors can estimate the LC50 values of the benzene derivatives reasonably well.

Predicting blood-brain transport of drugs: a computational approach.

PURPOSE: This study was conducted to determine the efficacy of using nonempirical parameters in the estimation of blood-brain transport, inferred from central nervous system (CNS) activity, for a set of twenty-eight compounds. METHODS: A discriminant function analysis was used to construct three distinct models based on topological indices, a hydrogen-bonding parameter, and logP. RESULTS: These models correctly predict the CNS activity of twenty-seven of the twenty-eight compounds. CONCLUSIONS: Nonempirical parameters may be used effectively in the estimation the cerebrovascular penetration for known and newly designed drugs.