Topochemical Descriptors Research Articles

Models for anti-tumor activity of bisphosphonates using refined topochemical descriptors

An in silico approach comprising of decision tree (DT), random forest (RF) and moving average analysis (MAA) was successfully employed for development of models for prediction of anti-tumor activity of bisphosphonates. A dataset consisting of 65 analogues of both nitrogen-containing and non-nitrogen-containing bisphosphonates was selected for the present study. Four refinements of eccentric distance sum topochemical index termed as augmented eccentric distance sum topochemical indices 1-4 [formula: see text] have been proposed so as to significantly augment discriminating power. Proposed topological indices (TIs) along with the exiting TIs (>1,400) were subsequently utilized for development of models for prediction of anti-tumor activity of bisphosphonates. A total of 43 descriptors of diverse nature, from a large pool of molecular descriptors, calculated through E-Dragon software (version 1.0) and an in-house computer program were selected for development of suitable models by employing DT, RF and MAA. DT identified two TIs as most important and classified the analogues of the dataset with an accuracy of 97% in training set and 90.7% in tenfold cross-validated set. Random forest correctly classified the analogues with an accuracy of 89.2%. Four independent models developed through MAA predicted the activity of analogues of the dataset with an accuracy of 87.6% to 89%. The statistical significance of proposed models was assessed through intercorrelation analysis, specificity, sensitivity and Matthew's correlation coefficient. The proposed models offer a vast potential for providing lead structures for development of potent anti-tumor agents for treatment of cancer that has spread to the bone.

Predicting allergic contact dermatitis: a hierarchical structure–activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors

A hierarchical classification study was carried out based on a set of 70 chemicals-35 which produce allergic contact dermatitis (ACD) and 35 which do not. This approach was implemented using a regular ridge regression computer code, followed by conversion of regression output to binary data values. The hierarchical descriptor classes used in the modeling include topostructural (TS), topochemical (TC), and quantum chemical (QC), all of which are based solely on chemical structure. The concordance, sensitivity, and specificity are reported. The model based on the TC descriptors was found to be the best, while the TS model was extremely poor.

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.

Quantitative structure-activity relationship modeling of insect juvenile hormone activity of 2,4-dienoates using computed molecular descriptors

Juvenile hormone (JH) activity of one hundred and eighty 2,4-dienoates reported for the larvae/pupae of six insect species was modeled using 915 atom pairs and 258 global molecular descriptors (topological and geometrical). Ridge regression, principal component regression and partial least square regression methods were used to model each of the JH activities. The use of all of the available parameters did not yield any good models, and extensive predictor trimming was necessary to improve the models. Ridge regression was found to give the best results among the three statistical tools used. The top ten molecular descriptors selected based on the t-statistic for each of the six models were found to be mostly atom pairs containing heteroatoms and topochemical descriptors. This suggests the importance of the chemical nature of the ligand rather than mere space-filling as the basis of the JH bioactivity. The residual plots indicate the existence of some non-linear relations, and recursive partitioning was used to capture any nonlinear relation between the bioassays and the molecular descriptors.

Prediction of partitioning properties for environmental pollutants using mathematical structural descriptors

Predictive models, based solely on molecular structure, were developed for three environ- mentally-related partitioning properties: Water solubility, soil/sediment partition coefficient, and octanol/water partition coefficient. Data for a diverse set of 136 chemicals were taken from the literature, and include aromatic and aliphatic compounds, as well as herbicides, pesticides, and polycyclic aromatic hydrocarbons. The hierarchical QSAR (HiQSAR) approach to model building was employed, in which increasingly more computer-resource intensive classes of structural descriptors are used only when the simpler and more easily calculable descriptors do not provide adequate models. The results indicate that the simple topostructural (TS) and topochemical (TC) descriptors provide the best models, and that, in many cases, these structure- based models are superior to those based on properties.

Prediction of Carbonic Anhydrase Activation by Tri‐/Tetrasubstituted‐pyridinium‐azole Compounds: A Computational Approach using Novel Topochemical Descriptor

AbstractA novel highly discriminating adjacency‐cum‐distance based topochemical descriptor, termed as Superadjacency topochemical index, has been derived and its discriminating power investigated with regard to activation of Carbonic anhydrase (CA) isozyme‐I by tri‐/tetrasubstituted‐pyridinium‐azole compounds. The new index is not only sensitive to the presence of heteroatoms but also overcomes the problem of degeneracy of many topological descriptors. The discriminating power of Superadjacency topochemical index was found to be far superior when compared with that of distance based Wiener‘s index and adjacency based Molecular connectivity index. The values of Wiener‘s index, path‐one Molecular connectivity index and Superadjacency topochemical index of each of the 42 substituted‐pyridinium‐azole compounds comprising the dataset were computed. Resultant data was analyzed and suitable models developed after identification of the active ranges. Subsequently, a biological activity was assigned to each of the compounds involved in the dataset using these models, which was then compared with the reported activation constants for Carbonic Anhydrase isozyme‐I. Excellent correlations were observed between the activation constants of CA isozyme‐I and all the topological/topochemical descriptors. The overall accuracy of prediction was about 91% for models based upon both Molecular connectivity index as well as Wiener‘s index, and 96% for model based upon Superadjacency topochemical index. Surprisingly, the accuracy of prediction in the active range was found to be 100% in all the models. Thus the proposed index offers a vast potential for structure‐activity/structure‐property studies.

Prediction of tissue-air partition coefficients: A comparison of structure-based and property-based methods

Three linear regression methods were used to develop models for the prediction of rat tissue-air partition coefficient ( P ). In general, ridge regression (RR) was found to be superior to principal component regression (PCR) and partial least squares regression (PLS). A set of 46 diverse low molecular-weight volatile chemicals was used to model fat-air, liver-air and muscle-air partition coefficients for male Fischer 344 rats. Comparisons were made between models developed using descriptors based solely on molecular structure and those developed using experimental properties, including saline-air and olive oil-air partition coefficients, as independent variables, indicating that the structure-property correlations are comparable to the property-property correlations. Multiple structure-based models were developed utilizing various classes of structural descriptors based on level of complexity, i.e. topostructural (TS), topochemical (TC), 3-dimensional (3D) and calculated octanol-water partition coefficient. In most cases, the structure-based models developed using only the TC descriptors were found to be superior to those developed using other structural descriptor classes. Haloalkane subgroups were modeled separately for comparative purposes, and although models based on the congeneric compounds were superior, the models developed on the complete sets of diverse compounds were acceptable. Comparisons were also made with respect to the types of descriptors important for partitioning across the various media.

Assessment of the Mutagenicity of Aromatic Amines from Theoretical Structural Parameters: A Hierarchical Approach

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 reslults show that the hierarchical QSAR approach using the algorithmically derived molecular descriptors can estimate the LC50 values of the benzene derivatives reasonably well.