A study of structure-carcinogenicity relationship for 86 compounds from NTP data base using topological indices as descriptors

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.

A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID study of the species differences in the carcinogenicity of benzene derivatives chemicals

A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID methods was applied as a tool to study and predict the mechanism of action of 100 carcinogenic benzene derivatives. Two 3D-QSAR models were obtained: (i) model of mouse carcinogenicity on the basis of 100 chemicals (model 1) and (ii) model of the differences in mouse and rat carcinogenicity on the basis of 73 compounds (model 2). 3D-QSAR regression maps indicated the important differences in species carcinogenicity, and the molecular positions associated with them. In order to evaluate the role of P450 metabolic process in carcinogenicity, the following approaches were used. The 3D models of CYP2E1 for mouse and rat were built up. A docking study was applied and the important ligand-protein residues interactions and oxidation positions of the molecules were identified. A new approach for quantitative assessment of metabolism pathways was developed, which enabled us to describe the species differences in CYP2E1 metabolism, and how it can be related to differences in the carcinogenic potential for a subset of compounds. The binding energies of the important substituents (local-binding energy-LBE) were calculated, in order to quantitatively demonstrate the contribution of the substituents in metabolic processes. Furthermore, a computational procedure was used for determining energetically favourable binding sites (GRID examination) of the enzymes. The GRID procedure allowed the identification of some important differences, related to species metabolism in CYP2E1. Comparing GRID, 3D-QSAR maps and LBE results, a similarity was identified, indicating a relationship between P450 metabolic processes and the differences in the carcinogenicity.

An atlas of forecasted molecular data. 2. Vibration frequencies of main-group and transition-metal neutral gas-phase diatomic molecules in the ground state

This atlas of diatomic-molecular vibration frequencies parallels the previously offered Atlas of Internuclear Separations. The Atlas was produced by mining the data from Huber and Herzberg and training neural network software to forecast new data. New protocols were employed with the powerful software, which was originally designed for forecasting the financial markets. The Atlas presents 1920 additional vibration frequencies for use until critical tables are available to fill the needs more precisely. The precision of the predictions is characterized by the average fractional 1% confidence limit, that is, 10.66%. The accuracies of the predictions are determined in two ways. First, 221 of the 224 Huber and Herzberg data values used for training and validation fall within the prediction confidence limits or fall outside by less than 10% of the Huber and Herzberg values, and 181 values agree (within the limits). Second, 87 of 101 comparison data values, consisting of literature data and some additional Huber and Herzberg values, fall within the prediction confidence limits or fall outside by less than half the prediction values, and 44 of the 101 values agree (within the limits).

Structure-mutagenicity modelling using counter propagation neural networks

The set of 95 aromatic amines and their mutagenic potency was treated with counter propagation neural network, which enables analysis of self-organising maps (SOMs) and also the prediction of mutagenicity. Compounds were described with four classes of descriptors: topostructural (TS), topochemical (TC), geometrical, and quantum chemical (QC). The models were tested on their prediction ability with leave-one-out (LOO) cross-validation method. The squares of correlation coefficient lie between 0.65 and 0.75 and are comparable with models obtained by linear methods. In addition, we analysed self-organising maps and found clusters of structurally similar compounds.

Joint QSAR analysis using the Free-Wilson approach and quantum chemical parameters

A new quantitative structure-activity relationship (QSAR) technique combining the Free-Wilson method and constructed quantum chemical parameters was used to simulate the aqueous solubility (Sw), 1-octanol/water partition coefficient (Kow) of 14 new synthesized benzanilide derivatives and their 96 h acute toxicity (EC50) to Daphnia magna. The mode of action of the 14 selected compounds to Daphnia magna was shown to be a complex process involving a physical partition stage and a bio-chemical reaction stage. The results also indicated that the joint (QSAR) analysis was much effective than the original Free-Wilson method and Hansch method not only in predicting properties/toxicity, but also in investigating the mode of action of chemicals.