2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma

Saı́z-Urra, Liane; González, Maykel Pérez; Teijeira, Marta

doi:10.1016/j.bmc.2007.02.032

Cited by 53 publications

(29 citation statements)

References 29 publications

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“…In a comparative study, where QSAR models are generated from the descriptors belonging to different categories, the FIT function, the AIC criterion and the LOF factor are very important parameters in explaining the best model. [52–54] In all above equations, the F -values remained significant at 99% level [ F 3,21 (0.01)=4.874] and indices q 2 LOO and q 2 L5O (>0.5) have accounted for their internal robustness. The r 2 Test value, greater than 0.5, specified that the identified test-set is able to validate these models externally.…”

Section: Resultsmentioning

confidence: 99%

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

Singh¹

2013

Indian J Pharm Sci

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The tumour necrosis factor-α converting enzyme inhibition activity of a series comprising of novel tartrate-based analogues has been quantitatively analysed in terms of molecular descriptors. The statistically validated quantitative structure-activity relationship models provided rationales to explain the inhibition activity of these congeners. The descriptors identified through combinatorial protocol in multiple linear regression analysis have highlighted the role of Moran autocorrelation of lag 7, weighted by atomic van der Waals volume, presence of both prime and nonprime amide carbonyl oxygen in the tartrate moiety and occurrence of five membered ring bearing substituents at varying sites. A few potential novel tartrate-based analogues have been suggested for further investigation.

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Section: Resultsmentioning

confidence: 99%

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

Singh¹

2013

Indian J Pharm Sci

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“…Hence, it can distinguish the details of important sub-structural differences. In the previous work, the 2D autocorrelation descriptors have been proven advantageous for establishing a QSAR model [49–53]. For the present work, the Moran’s index I [53,54] is employed for the classification of RSV inhibitors:

I = \frac{n}{2 L} \frac{\sum_{ij} δ_{italicij} false(p_{italicki} - {\bar{p}}_{k} false) false(p_{italickj} - {\bar{p}}_{k} false)}{\sum_{i} false(p_{italicki} - {\bar{p}}_{k} false)}

where n is the total number of data points; p ki and p kj are the values of physicochemical properties ( i.e.…”

Section: Resultsmentioning

confidence: 99%

A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest

Ming

Wang

et al. 2011

IJMS

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Experimental pEC50s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.

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“…Hence, it can distinguish the details of important substructural differences, however, the ''traditional'' descriptors (for example, log P or pK a ) cannot solve these questions. In the last decades, the 2D autocorrelation descriptor has been proven advantageous for establishing a QSAR model (Saíz-Urra et al, 2007;Caballero et al, 2006;Bauknecht et al 1994;Moreau and Broto, 1980). Three spatial autocorrelation vectors are employed for modeling inhibitory activities: Broto-Moreau's autocorrelation coefficients [ATS; Eq.…”

Section: Variables' Interpretation Of the Best Modelmentioning

confidence: 99%

“…Topological index, geometrical descriptors, and other descriptors are included in QSAR studies. The two-dimensional (2D) autocorrelation descriptor has been successfully used to construct same kinds of QSAR model for modeling biological activities (Saíz-Urra et al, 2007;Sharma et al, 2008;Caballero et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors using the PSO-SVM approach

Cheng

Zhang

2009

Med Chem Res

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In this work, the multiple linear regression (MLR) and support vector machine (SVM) methods were applied for modeling and predicting the inhibitory activity of imidazopyridine derivatives with two-dimensional (2D) autocorrelation descriptors calculated from the molecular structure alone for the first time. We define the new objective function as fitness, and it can guide the particle swarm optimization (PSO) method to select important descriptors which are responsible for the inhibitory activity of these compounds. The square of the correlation coefficient (R 2 = 0.897), the square of correlation coefficients of the test set R 2 ext ¼ 0:660 À Á , and the obtained statistical parameter of the calibrating set in the PSO-SVM model was 0.743, which demonstrated the reliability of the model. The PSO-SVM model is superior over the PSO-MLR method in the dataset with imidazopyridine derivatives as Et-PKG inhibitors. Our best quantitative structure-activity relationship model illustrates the importance of an adequate distribution of atomic properties represented in topological frames and reveals atomic masses, van der Waals volumes, Sanderson electronegativities, and polarizabilities to be the most influential atomic properties in the structures of the imidazopyridine derivatives.

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2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma

Cited by 53 publications

References 29 publications

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

Molecular Descriptors in Modelling the Tumour Necrosis Factor-α Converting Enzyme Inhibition Activity of Novel Tartrate-Based Analogues

A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest

QSAR studies of imidazopyridine derivatives as Et-PKG inhibitors using the PSO-SVM approach

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