Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, P. A.; Markopoulos, John; Igglessi‐Markopoulou, Olga

doi:10.1007/s10822-006-9038-2

Cited by 37 publications

(26 citation statements)

References 23 publications

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“…This test ensures the robustness of a QSAR model [19] and to assess the multiple linear regression models obtained by descriptor selection [20]. In y-randomization test, the dependent variable or y-data is randomly shuffled and a new QSAR model is developed keeping X-data intact.…”

Section: Y-randomizationmentioning

confidence: 99%

In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors

Nirmala¹,

Yesubabu²,

Seetharamaiah³

2015

IJCA

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QSAR(Quantitative Structure Activity Relationship) studies were carried out on a set of 72 α-sulfone hydroxamatesas Matrix Metalloproteinase-13 (MMP-13) inhibitors using multiple regression procedure. Outliers were removed based on Relative Error calculation and Extent of Extrapolation. The activity contributions of these compounds were determined from regression equation and the validation procedures such as external set cross-validation r 2 , (R 2 cv, ext ) and the regression of observed activities versus predicted activities and vice versa for validation set was described to analyze the predictive ability of the QSAR model. Parameters concerning predictive ability of QSAR model and Y-randomization tests were found to be within the limits. From a set of 5 models, an accurate and reliable QSAR model involving six descriptors was chosen based on the FIT Kubinyi function, which defines the statistical quality of the model. The generated model could be useful in designing more potent inhibitors of MMP-13.

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Section: Y-randomizationmentioning

confidence: 99%

In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors

Nirmala¹,

Yesubabu²,

Seetharamaiah³

2015

IJCA

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“…Aiming at overcoming the inherent limitations, the integration of VS and QSAR strategies provides useful opportunities to partially fulfill each method limitation, as well as allows the capture and incorporation of valuable information for the design of new small-molecule drug candidates. A variety of studies describing the integration of these drug design techniques has been reported in the literature [91][92][93][94][95][96]. For example, investigations were conducted for the discovery of inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGR) [91], through the integration of 3D QSAR CoMFA (Comparative Molecular Field Analysis) models [97] and FlexE [98] pre-filters based on energy score.…”

Section: Integrating Vs and Qsarmentioning

confidence: 99%

“…In this approach, QSAR models would be capable of identifying most probable nonbinders in docking databases, and also be used together with other pre-docking filters. Similarly, MLR (Multiple Linear Regression) models were developed for a series of chemokine receptor (CCR5) modulators, in order to create a useful alternative to filter out dissimilar compounds and to identify novel potent compounds [94].…”

Section: Integrating Vs and Qsarmentioning

confidence: 99%

“…Owing to the wide application of QSAR methods, a huge number of molecular descriptors is available. Based on that, techniques that avoid the conformational and alignment ambiguities inherent to 3D QSAR methods have also been employed to produce predictive models useful for database mining and VS [94][95][96]. In this context, 3D and 2D QSAR models (CoMFA [97], VolSurf [100] and Hologram QSAR [101]) were assessed for the prediction of new ligands towards the nuclear receptor aryl hydrocarbon receptor (AhR) [95].…”

Section: Integrating Vs and Qsarmentioning

confidence: 99%

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Virtual Screening and Its Integration with Modern Drug Design Technologies

Güido¹,

Oliva²,

Andricopulo

2008

CMC

164

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Drug discovery is a highly complex and costly process, which demands integrated efforts in several relevant aspects involving innovation, knowledge, information, technologies, expertise, R&D investments and management skills. The shift from traditional to genomics-and proteomics-based drug research has fundamentally transformed key R&D strategies in the pharmaceutical industry addressed to the design of new chemical entities as drug candidates against a variety of biological targets. Therefore, drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. The combination of available knowledge of several 3D protein structures with hundreds of thousands of small-molecules have attracted the attention of scientists from all over the world for the application of structure-and ligand-based drug design approaches. In this context, virtual screening technologies have largely enhanced the impact of computational methods applied to chemistry and biology and the goal of applying such methods is to reduce large compound databases and to select a limited number of promising candidates for drug design. This review provides a perspective of the utility of virtual screening in drug design and its integration with other important drug discovery technologies such as high-throughput screening (HTS) and QSAR, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.

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“…Literature survey showed that previously 3D-QSAR and virtual screening studies of CCR5 antagonists 1-(3,3-diphenyl- 18,19 They shows that for high affinity binders key chemical and structural requirement can be identified using physicochemical parameter, topological property and 3D field such as steric, electrostatics, hydrophobic, hydrogen bond donor/acceptor around a set of aligned ligand molecules. 3D-QSAR models, CoMFA and CoMSIA on a series of piperidine-based CCR5 antagonists have been developed by Song et al 20 Whereas, Y. Zhuo et al performed 3D-QSAR study for 1,3,4-trisubstituted pyrrolidine based CCR5 inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Ligand Based CoMFA, CoMSIA and HQSAR Analysis of CCR5 Antagonists

Gadhe¹,

Lee²,

Madhavan³

et al. 2010

Bulletin of the Korean Chemical Society

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In this study, we have developed QSAR models for a series of 38 piperidine-4-carboxamide CCR5 antagonists using CoMFA, CoMSIA and HQSAR methods. Developed models showed good statistics in terms of q 2 and r 2 values. Best predictions obtained with standard CoMFA model (r 2 = 0.888, q 2 = 0.651) and combined CoMSIA model (r 2 = 0.892, q 2 = 0.665) with electrostatics and H-bond acceptor parameter. The validity of developed models was assessed by test set of 9 compounds, which showed good predictive correlation coefficient for CoMFA (0.804) and CoMSIA (0.844). Bootstrapped analysis showed statistically significant and robust CoMFA (0.968) and CoMSIA (0.936) models. Best HQSAR model was obtained with a q 2 of 0.662 and r 2 of 0.936 using atom, connection, hydrogen, donor and acceptor as parameters and fragment size (7-10) with optimum number of 6 components. Predictive power of developed HQSAR model was proved by test set and it was found to be 0.728.

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Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques

Cited by 37 publications

References 23 publications

In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors

In Silico Multivariate Regressio n Analysis and Validation Studies on Selective MMP-13 Inhibitors

Virtual Screening and Its Integration with Modern Drug Design Technologies

Ligand Based CoMFA, CoMSIA and HQSAR Analysis of CCR5 Antagonists

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