Topochemical Indices Research Articles

(Q)SPR Models for Prediction of Hydrophobicity of Isatins

In the present study, the application of a wide variety of topological descriptors was investigated for predicting hydrophobicity (clogP) of isatin analogues. A total of four topochemical indices selected through decision tree (DT) were used for the development of single index based models using moving average analysis (MAA). The overall accuracy of prediction varied from a minimum of 95% to a maximum of 98% with regard to hydrophobicity.The values of sensitivity, specificity and Mathew's correlation coefficient for all MAA based models with regard to hydrophobicity (clogP) was found to be =78%, =94% and =0.85 respectively, suggesting robustness of proposed models. Since the compounds with high clogP values were found effective in carboxylesterases (CEs) inhibition, therefore, highly hydrophobic ranges of proposed MAA models can easily be exploited for the design and development of potent CEs inhibitors.

Fourth generation detour matrix-based topological descriptors for QSAR/QSPR - Part-2: application in development of models for prediction of biological activity

Augmented path eccentric connectivity topochemical indices (reported in part-1 of the manuscript) along with 42 diverse non-correlating molecular descriptors (shortlisted from a large pool of 2D and 3D MDs) were successfully utilised for the development of models through decision tree, random forest and moving average analysis for the prediction of antitubercular activity of aza and diazabiphenyl analogues of active compound (6S)-2-Nitro-{[4-(trifluoromethoxy)benzyl]oxy}-6,7-dihydro-5H-imidazo[2,1-b][1,3] oxazine (PA-824). The statistical significance of the proposed models was assessed through overall accuracy of prediction, intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient (MCC). The accuracy of prediction of the proposed models varied from a minimum of 81% to a maximum of ∼99%. High accuracy of prediction amalgamated with high MCC values clearly indicates robustness of the proposed models. The said models offer a vast potential for providing lead structures for the development of potent antitubercular drugs.

Diverse Models for the Prediction of HIV Integrase Inhibitory Activity of Substituted Quinolone Carboxylic Acids

In the present study both classification and correlation techniques of diverse nature were successfully employed for the development of models for the prediction of human immunodeficiency virus (HIV) integrase inhibitory activity using a dataset comprising 50 analogs of quinolone carboxylic acid. The values of various molecular descriptors (MDs) for each analog in the dataset were computed using the MDS V-life science QSAR plus module. The values of other MDs which are not part of MDS V-life science were computed using an in-house computer program. A decision tree (DT) was constructed for the HIV integrase inhibitory activity to determine the importance of MDs. The DT learned the information from the input data with an accuracy of 98% and correctly predicted the cross-validated (10 fold) data with an accuracy of 96%. Three MDs, E-state contribution descriptor (SssOHE), molecular connectivity topochemical index ($\chi {}^{{\rm A}} $), and eccentric connectivity topochemical index ($\xi _{{\rm C}}^{{\rm C}} $), were used to develop the models using moving average analysis (MAA). The accuracy of classification of single descriptor based models using MAA was found to vary from a minimum of 96% to a maximum of 98%. The statistical significance of the models was assessed through specificity, sensitivity, overall accuracy, Mathew's correlation coefficient, and intercorrelation analysis. The widely used methods like multiple linear regression, partial least squares, and principal component regression were employed for development of correlation models. The models were generated on a training set of 36 molecules. The models had a correlation coefficient (r(2) ) of 0.86 to 0.92, significant cross validated correlation coefficient (q(2) ) of 0.79 to 0.85, F-test from 63.2 to 93.06, r(2) for external test set (pred_r(2) ) from 0.69, coefficient of correlation of predicted dataset (pred_ r(2) Se) of 0.77, and degree of freedom from 27 to 30. Alignment independent descriptors, SsOHE-index, SaaCHE index, SssCH2, and x log P were found to be the most important descriptors for the development of correlation models for the prediction of HIV integrase inhibitory activity.

Fourth generation detour matrix-based topological indices for QSAR/QSPR - Part-1: development and evaluation

In the present study, four detour matrix-based Topological Indices (TIs) termed as augmented path eccentric connectivity indices 1-4 (denoted by (AP)ξ(1)(C), (AP)ξ(2)(C), (AP)ξ(3)(C) and (AP)ξ(4)(C)) as well as their topochemical versions (denoted by (AP)ξ(1c)(C), (AP)ξ(2c)(C), (AP)ξ(3c)(C) and (AP)ξ(4c)(C)) have been conceptualised. A modified detour matrix termed as chemical detour matrix (Δ(c)) has also been proposed so as to facilitate computation of index values of topochemical versions of the said TIs. Values of the proposed TIs were computed for all the possible structures containing three, four and five vertices using an in-house computer program. The said TIs exhibited exceptionally high discriminating power and high sensitivity towards branching/relative position of substituent(s) in cyclic structures amalgamated with negligible degeneracy. Due care was taken during the development of TIs so as to ensure that reduction in index values of complex chemical structures to be within reasonable limits without compromising discriminating power. The mathematical properties of one of the proposed TIs have also been studied. With exceptionally high discriminating power, high sensitivity towards branching as well as relative position(s) of substituents in cyclic structures and negligible degeneracy, the proposed indices offer a vast potential for use in characterisation of structures, similarity/dissimilarity studies, lead identification and optimisation, combinatorial library design and quantitative structure-activity/property/toxicity/pharmacokinetic relationship studies.

Superaugmented Eccentric Distance Sum Connectivity Indices: Novel Highly Discriminating Topological Descriptors for QSAR/QSPR

Four highly discriminating fourth-generation topological indices (TIs), termed as superaugmented eccentric distance sum connectivity indices, as well as their topochemical versions (denoted by , , and ), have been conceptualized in this study. The values of these indices for all possible structures with three, four, and five vertices containing one heteroatom were computed using an in-house computer program. The proposed superaugmented eccentric distance sum connectivity topochemical indices exhibited exceptionally high discriminating power, low degeneracy, and high sensitivity toward both the presence and the relative position of heteroatom(s) for all possible structures with five vertices containing at least one heteroatom. Intercorrelation analysis revealed the absence of correlation of proposed indices with Zagreb indices and the molecular connectivity index. Subsequently, the proposed TIs were successfully utilized for the development of models for the prediction of checkpoint kinase inhibitory activity of 2-arylbenzimidazoles. A data set comprising 47 differently substituted analogs of 2-arylbenzimidazoles was selected for the study. The values of various TIs for each analog in the data set were computed using an in-house computer program. The resulting data were analyzed, and suitable models were developed through decision tree (DT), random forest (RF), and moving average analysis (MAA). The performance of the models was assessed by calculating the specificity, sensitivity, overall accuracy, and Mathew's correlation coefficient. A decision tree was constructed for the checkpoint kinase inhibitory activity to determine the importance of topological indices. The decision tree identified the proposed TIs -, - as the most important indices. The decision tree learned the information from the input data with an accuracy of 96% and correctly predicted the cross-validated (10-fold) data with an accuracy of 77%. Random forest correctly predicted the checkpoint kinase inhibitory activity with an accuracy of 83%. The single index-based models were also developed for the prediction of checkpoint kinase inhibitory activity using MAA. The accuracy of prediction of single index-based models derived through MAA was found to vary from a minimum of 90% to a maximum of 95%. Exceptionally high discriminating power, low degeneracy, and high sensitivity toward branching and presence of heteroatom of proposed indices can be of immense use in drug design, isomer discrimination, similarity/dissimilarity studies, quantitative structure activity/property relationships, lead optimization, and combinatorial library design.

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.

Quantitative Structure-Activity Relationships for Anticancer Activity of 2- Phenylindoles Using Mathematical Molecular Descriptors

Calculated atom pairs (APs) and a set of 369 topological indices (TIs) calculated by POLLY, Triplet, and Molconn-Z software were used to develop QSARs for anticancer activity of a group of 2-phenyl indoles. The TIs included both topostructural (TS) and topochemical (TC) indices. Results show that ridge regression using TS indices, TC indices, and atom pairs produced high-quality models for the prediction of anticancer activity of a set of 89 phenylindole derivatives. Quality of QSARs derived in this paper is comparable or superior to both CoMFA and other statistical models reported for 2-phynylindoles in the earlier published literature. Easily calculated molecular descriptors like TIs and APs used in this paper may find application in the QSAR and in silico prediction of bioactivity of new phenylindole derivatives.

Improved superaugmented eccentric connectivity indices

Topochemical versions of all the four superaugmented eccentric connectivity indices (denoted by: SAc ξ4 c, SAc ξ5 c, SAc ξ 6 c , and SAc ξ 7 c ) were utilized for the development of models for prediction of hiCE and hCE1 inhibitory activities. The values of these topochemical indices were computed for each of the 65 analogs constituting the data set using an in-house computer program. Resulting data was analyzed and suitable models were developed after identification of the active ranges by maximization of moving average with regard to active derivatives. Subsequently, two biological activities were assigned to each analog using proposed models, which were then compared with the reported hiCE and hCE1 inhibitory activities. Statistical significance of topological indices/models was investigated through sensitivity, specificity, and Matthews correlation coefficient (MCC). The overall accuracy of prediction varied from a minimum of 81% for a model based upon SAc ξ 4 c to a maximum of 92% in case of a model based upon SAc ξ 5 c with regard to hiCE inhibitory activity and from a minimum of 85% for a model based upon SAc ξ 4 c to a maximum of 94% in case of a model based upon SAc ξ 7 c with regard to hCE1 inhibitory activity. An excellent relationship between new generation superaugmented eccentric connectivity topochemical indices ( SAc ξ 4 c , SAc ξ 5 c , SAc ξ 6 c , and SAc ξ 7 c ) and hiCE and hCE1 inhibitory activities can be attributed to the sensitivity of the proposed topological indices toward nature, number, and relative position of heteroatom. High predictability amalgamated with high potency of the active ranges offer proposed models a vast potential for providing lead structures for development of potent and selective hiCE and hCE1 inhibitors.

A QSAR Study of HIV Protease Inhibitors Using Theoretical Descriptors

This paper reports the development of quantitative structure-activity relationship (QSAR) models for a set of 170 chemicals using mathematical descriptors which can be calculated directly from molecular structure without the input of any other experimental data. The calculated descriptors include topostructural (TS), topochemical (TC), and quantum chemical (QC). Because the situation is rank deficient i.e. the number of independent variables (descriptors) is larger than the number of compounds, three robust linear statistical modeling methods capable of handling such situations, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR) were used for QSAR formulation. Results show that PLS and RR gave better q2 values as compared to the PCR method. Of the three classes of descriptors, the TC indices were the best predictors of anti-HIV activity and the QC indices were the least effective.

PLS and N-PLS-based MIA-QSTR modelling of the acute toxicities of phenylsulphonyl carboxylates to Vibrio fischeri

Descriptors based on multivariate image analysis have been used to derive predictive QSTR models of the acute toxicities of phenylsulphonyl carboxylates to Vibrio fischeri. Classical and multilinear partial least squares, PLS and N-PLS, respectively, were applied as regression methods, demonstrating similar predictive capability to each other. Model performance was improved in c. 10% after removing an outlier, and the results were in general agreement with the ones previously obtained through CoMFA and extended topochemical atom indices analysis. Overall, this study showed that a simple procedure is able to give highly predictive models, useful in ecotoxicology, independent of the regression method used for this class of compounds.

Models for antitubercular activity of 5â-O-[(N-Acyl)sulfamoyl]adenosines.

The relationship between topological indices and antitubercular activity of 5′-O-[(N-Acyl)sulfamoyl]adenosines has been investigated. A data set consisting of 31 analogues of 5′-O-[(N-Acyl)sulfamoyl]adenosines was selected for the present study. The values of numerous topostructural and topochemical indices for each of 31 differently substituted analogues of the data set were computed using an in-house computer program. Resulting data was analyzed and suitable models were developed through decision tree, random forest and moving average analysis (MAA). The goodness of the models was assessed by calculating overall accuracy of prediction, sensitivity, specificity and Mathews correlation coefficient. Pendentic eccentricity index – a novel highly discriminating, non-correlating pendenticity based topochemical descriptor – was also conceptualized and successfully utilized for the development of a model for antitubercular activity of 5′-O-[(N-Acyl)sulfamoyl]adenosines. The proposed index exhibited not only high sensitivity towards both the presence as well as relative position(s) of pendent/heteroatom(s) but also led to significant reduction in degeneracy. Random forest correctly classified the analogues into active and inactive with an accuracy of 67.74%. A decision tree was also employed for determining the importance of molecular descriptors. The decision tree learned the information from the input data with an accuracy of 100% and correctly predicted the cross-validated (10 fold) data with accuracy up to 77.4%. Statistical significance of proposed models was also investigated using intercorrelation analysis. Accuracy of prediction of proposed MAA models ranged from 90.4 to 91.6%.

Predicting Anti‐HIV Activity of Dimethylaminopyridin‐2‐ones: Computational Approach using Topochemical Descriptors

In this study, relationship between the topochemical indices and anti-HIV activity of dimethylaminopyridin-2-ones has been investigated. Three topochemical indices of diverse nature, i.e. Wiener's topochemical index--a distance-based topochemical descriptor, molecular connectivity topochemical index--an adjacency based topochemical descriptor and augmented eccentric connectivity topochemical index--an adjacency-cum-distance-based topochemical descriptor, were used for the present investigations. The values of the Wiener's topochemical index, molecular connectivity topochemical index and augmented eccentric connectivity topochemical index for each of the 103 analogues comprising the data set were computed using an in-house computer program. Resulting data were analyzed and suitable models were developed after the identification of the active ranges. Subsequently, a biological activity was assigned to each compound using these models, which was then compared with the reported anti-HIV activity. Statistical significance of proposed models was further investigated using chi-squared test and intercorrelation analysis. Accuracy of prediction of anti-HIV activity was found to vary from 81 to 85% using these models.

Topological models for prediction of physico-chemical, pharmacokinetic and toxicological properties of antihistaminic drugs using decision tree and moving average analysis

Various topostructural and topochemical indices were used to encode the structureal features of antihistaminic drugs. The values of 18 indices for each drug comprising the dataset were computed using an in-house computer program. In the present study, decision tree and moving average analysis were used to predict physico-chemical (log P), pharmacokinetic (T(max)) and toxicological properties (LD(50)) of antihistaminic drugs. A decision tree was constructed for each property to determine the importance of Topological Indices (TIs). Single topological index based models were developed using moving average analysis. The tree learned the information from the input data with an accuracy of >94% and predicted the cross-validated (10-fold) data with an accuracy of upto 71%. Moving average analysis resulted in single index based models with an accuracy upto 80%.

TOPOCHEMICAL MODELS FOR THE PREDICTION OF LIPOPHILICITY OF 1,3-DISUBSTITUTED PROPAN-2-ONE ANALOGS

In the present study, the relationship between the topochemical indices and log P values of 1,3-disubstituted propan-2-one analogs 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. The values of the Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index were computed for each of the 45 analogs constituting the data set using an in-house computer program. The predicted log P values using leave-one-out (LOO) procedure exhibited a q2 of 0.72, 0.70, and 0.71 with reported log P values for Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index, respectively. Separate models were developed using training set and log P of each analog in the independent test set was predicted using these models. The correlation of predicted log P values with the reported values, for independent test set, were in good agreement with those predicted using LOO procedure.

Predicting anti-HIV-1 activity of 6-arylbenzonitriles: Computational approach using superaugmented eccentric connectivity topochemical indices

Highly discriminating adjacency-cum-distance based topochemical indices termed as superaugmented eccentric connectivity topochemical indices for quantitative structure–activity and structure–property relationships (QSAR/QSPR) have been conceptualized in the present study. These indices were found to exhibit high sensitivity towards the presence and relative position of heteroatom(s), exceptionally high discriminating power and negligible degeneracy for all possible structures of five vertices containing one heteroatom. Utility of these indices was investigated for development of models for prediction of anti-human immunodeficiency virus (HIV)-1 activity using a data set comprising 81 differently substituted 6-arylbenzonitriles. The values of the superaugmented eccentric connectivity topochemical indices of all the analogues comprising the data set were computed 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 to each analogue using these models which was then compared with the reported anti-HIV-1 activity. The accuracy of prediction was found to be ∼81% for all the three topochemical models. High sensitivity towards presence and relative position of heteroatom(s), exceptionally high discriminating power amalgamated with low degeneracy offer proposed topochemical indices vast potential for isomer discrimination, similarity/dissimilarity, drug design, quantitative structure–activity/structure–property relationships, lead optimization and combinatorial library design.

Topochemical Models for Prediction of Telomerase Inhibitory Activity of Flavonoids

In the present study, the relationship between the topochemical indices and telomerase inhibitory activity of flavonoids 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 superadjacency topochemical index (an adjacency cum distance-based topochemical descriptor) were used for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index and superadjacency topochemical index for each of the 30 analogues comprising the data set were computed using an in-house computer program. Resultant data was analysed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue involved in the data set using these models, which was then compared with the reported telomerase inhibitory activity. Statistical significance of proposed models was investigated using intercorrelation analysis. Accuracy of prediction using proposed models was found to vary from 80% to 83%.

Predicting anti-allergic activity of 4-oxopyrimido [4,5-b]quinolines: Computational approach using topochemical indices

The relationship between the topochemical indices and antiallergic activity of 4-oxopyrimido[4,5-b]quinolines was 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 49 analogs of substituted 4-oxopyrimido[4,5-b]quinolines was selected for the present study. The values of the Wiener’s topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index for each of the 49 analogs comprising the data set were computed. The resulting data were analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analog involved in the data set via these models, which was then compared with the reported antiallergic activity. Accuracy of prediction was found to vary from a minimum of approximately 86% to a maximum of approximately 88%. These models offer vast potential for providing lead structures for the development of potent antiallergic agents.

Topochemical models for the prediction of poly(ADP-ribose) polymerase inhibitory activity of indole-1-ones

The relationship between the topochemical indices and poly(ADP-ribose) polymerase inhibitory activity of indole-1-ones has been investigated. Wiener’s topochemical index, a distance-based topochemical descriptor; molecular connectivity topochemical index, an adjacency-based topochemical descriptor; and superadjacency topochemical index, an adjacency-cum-distance–based topochemical descriptor, were used for the present investigation. The values of Wiener’s topochemical index, molecular connectivity topochemical index, and superadjacency topochemical index for each of 41 analogs comprising the data set were computed via an in-house computer program. Resulting data were analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analog involved in the data set using these models, which was then compared with the reported poly(ADP-ribose) polymerase inhibitory activity. Statistical analysis revealed that two of the three models were not intercorrelated. Accuracy of prediction of these models varied from approximately 83% to 85%.

Topochemical models for anti-HIV activity of 1-alkoxy-5-alkyl-6-(arylthio)uracils

AbstractRelationship between topochemical indices and anti-HIV-1 (HTLV-III) inhibitory activity of 1-alkoxy-5-alkyl-6-(arylthio)uracils has been studied. Superadjacency, an adjacency-cum-distance-based, topochemical index and Wiener’s, a distance-based, topochemical index were calculated for a set of 36 1-alkoxy-5-alkyl-6-(arylthio)uracils. Resulting data were 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 and compared with the reported anti-HIV-1 activity. High accuracy of prediction was observed using these models. Statistical analysis revealed significance of the proposed models as well as a lack of correlation between the topochemical indices employed for the chosen data set.

Validation of topochemical models for the prediction of permeability through the blood-brain barrier

Recently published topochemical models for permeability through the blood-brain barrier were validated and cross-validated in the present study. Five models based on 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, for permeability of structurally and chemically diverse molecules through blood-brain barrier were used in the present investigation. A data set comprising 62 structurally and chemically diverse compounds was selected. This data set was divided into two sets of 31 compounds each - one to serve as the validation set and other as the cross-validation set. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 31 compounds of the validation set were computed using an in-house computer program. Resultant data was analyzed and each compound was assigned a permeability characteristic using topochemical models, which was then compared with the reported permeability through the blood-brain barrier. Accuracy of prediction of these models was calculated. The same procedure was similarly followed for the cross-validation set. Studies revealed accuracy of prediction of the order of 70-80% during validation. Surprisingly, very high predictability of the order of 77-91% was observed during cross-validation. High predictability observed during validation as well as cross-validation authenticates topochemical models for prediction of permeability through the blood-brain barrier.