Topochemical Indices Research Articles

Topochemical models for predicting the activity of α,γ-diketo acids as inhibitors of the hepatitis c virus NS5B RNA-dependent RNA polymerase

A relationship between the topochemical indices of α,γ-diketo acids and their inhibitory activity with respect to the hepatitis C virus NS5b RNA-dependent RNA polymerase has been studied. The values of Wiener’s topochemical index W c (a distance based descriptor), the Zagreb topochemical index M 2 c (an adjacency based descriptor), and the topochemical eccentric connectivity index ξ c c (an adjacency-cum-distance based descriptor) were calculated for a set of 30 compounds using an in-house computer program. The resulting data array was analyzed and the models of activity were developed after determination of the active ranges of parameters. Subsequently, a biological activity was assigned using these models to each compound included in the data bank, and the assignment was compared with data reported on the inhibitory activity. A high accuracy of prediction was observed for the proposed models. These models possess vast potential in providing basis structures for the development of diketo acids capable of effectively inhibiting the hepatitis C virus NS5b RNA-dependent RNA polymerase.

Topochemical models for the prediction of voltage-gated sodium channel binding activity of hydantoins and related non-hydantoins

The relationship of Wiener's topochemical index-a distance based topochemical index, molecular connectivity topochemical index-an adjacency based topochemical index and eccentric connectivity topochemical index-an adjacency-cum-distance based topochemical index with sodium channel binding activity has been investigated. A dataset comprising 50 hydantoins and related non-hydantoins was selected. The dataset was divided equally into training and test sets. The values of the three topochemical indices for all the compounds present in both the training and test sets were computed using an in-house computer program. The resulting data was analyzed and suitable models were developed after identification of the active ranges in the training set. Subsequently, a biological activity was assigned to each compound involved in the training set using these models, which was then compared with the reported sodium channel binding activity. An accuracy of prediction of the order of >99% was observed using the proposed models. Cross-validation of these models using the test set revealed an exceptionally high accuracy of approximately 95%.

Topochemical models for the prediction of permeability through blood–brain barrier

Relationship between the topochemical indices and permeability of diverse series of compounds through blood–brain barrier has been investigated. Three-topochemical indices, Wiener's topochemical index—a distance-based topochemical descriptor, molecular connectivity topochemical index—an adjacency based topochemical descriptor and eccentric connectivity topochemical index—an adjacency-cum-distance based topochemical descriptor, were used for the present investigation. A data set comprising of 28 compounds with established CNS permeation tendency was selected for present study. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 28 compounds comprising the data set were computed using an in-house computer program. Resultant data was analyzed and suitable models were developed after identification of the permeable ranges. Subsequently, permeability characteristic was assigned to each compound involved in the data set using these models, which was then compared with the reported permeability through blood–brain barrier. Accuracy of prediction was found to vary from a minimum of 83% to a maximum of ∼95% using these models.

Prediction of h5-HT 2A receptor antagonistic activity of arylindoles: Computational approach using topochemical descriptors

Relationship between the topochemical indices and h5-HT 2A receptor antagonistic activity of arylindoles has been investigated. Three topochemical indices, Wiener's topochemical index – a distance-based topochemical descriptor, molecular connectivity topochemical index – an adjacency-based topochemical descriptor and eccentric connectivity topochemical index – an adjacency-cum-distance based topochemical descriptor, were used for the present investigation. A data set comprising 31 differently substituted arylindoles was selected for the present study. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index were computed for all the analogues involved in the data set using an in-house computer program. Resultant data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue using these models, which was then compared with the reported h5-HT 2A receptor antagonistic activity. Accuracy of prediction was found to vary from a minimum of ∼81% to a maximum of ∼84%.

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

Vapor pressure is an important property which is an indicator of chemical volatility, along with transport, partitioning, fate and distribution of environmental pollutants. Various models have been developed for the prediction of vapor pressure of chemicals using physicochemical and calculated structural properties. We have used different classes of graph theoretic indices, e.g., topostructural indices, topochemical indices, geometrical (3D) indices and, quantum chemical descriptors, for the development of predictive models for vapor based on a structurally diverse set of 469 chemicals. Initially, a set of 379 molecular descriptors was calculated using the software POLLY, Triplet, Sybyl, MOPAC, and Molconn-Z. Comparatively, three linear regression methodologies were used to develop hierarchical QSAR (HiQSAR) models, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression. The results indicate that, in general, RR outperforms PCR and PLS, and that the easily calculated topological descriptors are sufficient for the prediction of vapor pressure based on this large, diverse set of chemicals.

Topochemical models for prediction of cyclin-dependent kinase 2 inhibitory activity of indole-2-ones

The relationship between the topochemical indices and cyclin-dependent kinase 2 (CDK2) inhibitory activity of indole-2-ones has been investigated. The relationship of topochemical versions of well known topological indices of Wiener's index--a distance-based topological descriptor, molecular connectivity index, an adjacency-based topological descriptor and eccentric connectivity index--an adjacency-cum-distance based topological descriptor with CDK2 inhibitory activity of indole-2-ones has been investigated. A data set comprising 67 analogues of substituted indole-2-ones was selected for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index for each of 67 analogues comprising the data set were computed. The resulting data was analyzed and suitable models developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue in the data set using these models, which was then compared with the reported CDK2 inhibitory activity. Accuracy of prediction was found to vary from a minimum of 88% for a model based upon molecular connectivity topochemical index to a maximum of approximately 90% for model based upon eccentric connectivity topochemical index.

Topochemical Models for Prediction of Anti-tumor Activity of 3-Aminopyrazoles

Relationship between topochemical indices and inhibition of CDK2/cyclin A by 3-aminopyrazoles was investigated using a data set comprising of 42 3-aminopyrazoles. Three topochemical indices--the Wiener's topochemical index--a distance based topochemical index, atomic molecular connectivity index--an adjacency based topochemical index and superadjacency topochemical index--an adjacency-cum-distance based topochemical index were used for the present investigations. The values of Wiener's topochemical index, atomic molecular connectivity index and superadjacency topochemical index for each of the 42 compounds comprising the data set were computed using an in-house computer program. Resultant data was subsequently analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each of the compounds using these models, which was then compared with the reported CDK2/cyclin A inhibitory activity. High accuracy of prediction ranging from 86 to 89% was observed using these models.

Topochemical Models for Prediction of Anti-HIV Activity of 4-Benzyl Pyridinone Derivatives

ABSTRACTRelationship between topochemical indices and anti-HIV activity of 4-Benzyl pyridinone derivatives has been investigated. The values of molecular connectivity topochemical index (an adjacency based topochemical descriptor) Wiener's topochemical index (a distance based topochemical descriptor) and superadjacency topochemical index (an adjacency cum distance based topochemical descriptor) were calculated for each of the 32 compounds comprising the data set using an in-house computer program. The resultant data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned using these models to each of the compounds involved in the dataset which was then compared with the reported anti-HIV activity. Exceptionally high accuracy of prediction was observed using these models. These models offer vast potential for providing lead structures for the development of potent anti-HIV agents.

Topochemical model for prediction of anti-HIV activity of HEPT analogs

The relationship between the superadjacency topochemical index and the anti-HIV activity of HEPT analogs has been investigated in the present study. The values of superadjacency topochemical index of all the analogs involved in the data set were calculated using an in-house computer program. Resulting data were analyzed and a suitable model was developed after identification of the active range. Subsequently, a computed biological activity was assigned to each of the compounds involved in the dataset, which was then compared with the reported anti-HIV activity. Accuracy of prediction was found to be 88% using the said model. The predictive ability of the model indicates that this model can be used for predicting the anti-HIV activity of the compounds prior to synthesis and may prove to be highly beneficial for providing lead structures for development of potent anti-HIV agents.

Predicting anti-HIV activity of 2,3-diaryl-1,3-thiazolidin-4-ones: computational approach using reformed eccentric connectivity index

The eccentric connectivity index, which has recently been employed successfully for the development of numerous mathematical models for the prediction of biological activities of diverse nature, has been reformed to overcome its limitations caused by degeneracy and insensitivity towards heteroatoms. The reformed eccentric connectivity index, termed the eccentric connectivity topochemical index, overcomes the limitations of the eccentric connectivity index by exhibiting very low degeneracy and displaying sensitivity to both the presence and relative position of heteroatoms without compromizing the discriminating power of the eccentric connectivity index. The relationship of the eccentric connectivity topochemical index, eccentric connectivity index and Wiener's index with regard to the anti-HIV activity of 2, 3-diaryl-1, 3-thiazolidin-4-one derivatives was subsequently investigated. The values of the eccentric connectivity topochemical index, the eccentric connectivity index and Wiener's index of each of 31 analogues comprizing the data set were computed using in-house computer program. Resultant data was analyzed and suitable models developed after identification of active ranges. Subsequently, each derivative was assigned a biological activity using these models, which was then compared with the reported anti-HIV activity. The accuracy of prediction using these models was found to vary from 81 to 90%. The proposed index offers a vast potential for virtual screening of combinatorial libraries, structure property/activity studies and drug design. [figure]. Basic structure of 2,3-diaryl-1, 3-thiazoidin-4-ones.

Predicting anti-HIV activity of phenethylthiazolethiourea (PETT) analogs: computational approach using Wiener's topochemical index

A modification of Wiener's index, termed as Wiener's topochemical index ( W c), has been conceptualized. This topochemical index is not only sensitive to the presence of heteroatoms but also exhibits far less degeneracy when compared to Wiener's index. The relationship of the Wiener's topochemical index and Wiener's index with the anti-HIV activity of phenethylthiazolethiourea (PETT) compounds was investigated. The values of both the indices for each of the 62 analogs comprising the dataset were computed and suitable models were developed after identification of the active ranges. Subsequently, each analog was assigned a biological activity using these models, which was then compared with the reported anti-HIV activity. Accuracy of prediction was found to be ∼90% using models based upon Wiener's index as well as Wiener's topochemical index.

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.

Predicting blood:air partition coefficients using theoretical molecular descriptors

Three regression methods, namely ridge regression (RR), partial least squares (PLS), and principal components regression (PCR), were used to develop models for the prediction of rat blood:air partition coefficient for increasingly diverse data sets. Initially, modeling was performed for a set of 13 chlorocarbons. To this set, 10 additional hydrophobic compounds were added, including aromatic and non-aromatic hydrocarbons. A set of 16 hydrophilic compounds was also modeled separately. Finally, all 39 compounds were combined into one data set for which comprehensive models were developed. A large set of diverse, theoretical molecular descriptors was calculated for use in the current study. The topostructural (TS), topochemical (TC), and geometrical or 3-dimensional (3D) indices were used hierarchically in model development. In addition, single-class models were developed using the TS, TC, and 3D descriptors. In most cases, RR outperformed PLS and PCR, and the models developed using TC indices were superior to those developed using other classes of descriptors.

Prediction of cellular toxicity of halocarbons from computed chemodescriptors: a hierarchical QSAR approach.

A hierarchical quantitative structure-activity relationship (HiQSAR) approach was used to estimate toxicity and genetic toxicity for a set of 55 halocarbons using computed chemodescriptors. The descriptors consisted of topostructural (TS), topochemical (TC), geometrical, semiempirical (AM1) quantum chemical, and ab initio (STO-3G, 6-31G(d), 6-311G, 6-311G(d), and aug-cc-pVTZ) quantum chemical indices. For the two toxicity endpoints investigated, ARR and D(37), the TC indices gave the best cross-validated R(2) values. The 3-D indices also performed either as well as or slightly superior to the TC indices. For the four categories of quantum chemical indices used for the development of predictive models, the AM1 parameters gave the worst performance, and the most advanced ab initio (B3LYP/aug-CC-pVTZ) parameters gave the best results when used alone. This was also the case when the quantum chemical indices were used in the hierarchical QSAR approach for both of the toxicity endpoints, ARR and D(37). The models resulting from HiQSAR are of sufficiently good quality to estimate toxicity of halocarbons from structure.

Prediction of Mutagenicity Utilizing A Hierarchical Qsar Approach

Quantitative structure-toxicity (QSTR) models for the mutagenicity of a set of 95 aromatic amines were developed using four classes of calculated molecular descriptors, viz., topostructural, topochemical, geometrical and quantum chemical indices. Topochemical indices gave the best predictive model when the different classes of parameters were used separately. When hierarchical QSTRs were developed using all four classes of descriptors, there was a significant increase in explained variance by the addition of topochemical indices to the set of independent variables. The addition of geometrical and quantum chemical indices or log P to the set of descriptors resulted in very little improvement in model quality.

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.

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 Study of Topological and Geometrical Parameters in Estimating Normal Boiling Point and Octanol/Water Partition Coefficient

We have used topological, topochemical and geometrical parameters in predicting: (a) normal boiling point of a set of 1023 chemicals and (b) lipophilicity (log P, octanol/water) of 219 chemicals. The results show that topological and topochemical variables can explain most of the variance in the data. The addition of geometrical parameters to the models provide marginal improvement in the model's predictive power. Among the three classes of descriptors, the topochemical indices were the most effective in predicting properties.