Mathematical structural descriptors and mutagenicity assessment: a study with congeneric and diverse datasets$

Quantitative bioactivity and toxicity assessment of chemical compounds plays a central role in drug discovery as it saves a substantial amount of resources. To this end, high-performance computing has enabled researchers and practitioners to leverage hundreds, or even thousands, of computed molecular descriptors for the activity prediction of candidate compounds. In this paper, we evaluate the utility of two large groups of chemical descriptors by such predictive modelling, as well as chemical structure discovery, through empirical analysis. We use a suite of commercially available and in-house software to calculate molecular descriptors for two sets of chemical mutagens - a homogeneous set of 95 amines, and a diverse set of 508 chemicals. Using calculated descriptors, we model the mutagenic activity of these compounds using a number of methods from the statistics and machine-learning literature, and use robust principal component analysis to investigate the low-dimensional subspaces that characterize these chemicals. Our results suggest that combining different sets of descriptors is likely to result in a better predictive model - but that depends on the compounds being modelled and the modelling technique being used.

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.

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.

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.

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.

Use of graph theoretic parameters in risk assessment of chemicals

In many instances of risk assessment, one has to estimate the potential risk of chemicals using limited experimental data, or no empirical data at all. In such cases, the use of non-empirical parameters, which can be calculated directly from structure, is a viable option for the risk assessor. Graph invariants have been used in predicting properties of congeneric sets of chemicals and determining structural similarity/dissimilarity of molecules. In this paper we have used (a) topological parameters in predicting mutagenicity of a diverse set of 520 chemicals and (b) graph theoretic parameters in quantifying structural similarity for a selection of analogs. The results of these analyses are presented along with a critical discussion of the utility and limitations of these methods.

Predicting mutagenicity of chemicals using topological and quantum chemical parameters: a similarity based study

Five molecular similarity methods have been used to estimate mutagenicity of a set of 73 aromatic and heteroaromatic amines. Two of the similarity methods (AP, PCTI) are based on topological parameters. Two other methods (PCPROP, PROP) are derived from physicochemical and electronic parameters. The fifth method, PCALL, is based on a combination of both topological and physicochemical parameters. The effectiveness of the five similarity methods in the rapid evaluation of mutagenicity is discussed.

Tolerance space and molecular similarity

Molecular similarity methods were used in selecting K nearest neighbors and in estimating mutagenicity of 95 aromatic amines and boiling points of a large set of over 2,900 compounds. Similarity is analyzed in terms of the concept of tolerance space. Specifically, the role of non-transitivity of the tolerance relation in estimating properties using similarity methods is examined.