Selectivity-based QSAR approach for screening and evaluation of TRH analogs for TRH-R1 and TRH-R2 receptors subtypes

Kadam, Rameshwar U.; Chavan, Archana G.; Monga, Vikramdeep; Kaur, Navneet; Jain, Rahul; Roy, Nilanjan

doi:10.1016/j.jmgm.2008.05.005

Cited by 2 publications

(4 citation statements)

References 39 publications

(39 reference statements)

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“…So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. , On the other hand, various in silico tools have been explored for the prediction of the receptor−ligand binding affinity. In more detail, the generation of quantitative structure−activity relationships (QSARs) represents one of the most applied and reliable approaches to mathematically correlate the molecular properties and the corresponding receptor−ligand binding affinity to precisely predict the binding affinity of new compounds. , However, only few pioneer studies suggest an integration of both traditional classification and regression analysis as a useful filtering strategy to select potent and selective ligands …”

Section: Introductionmentioning

confidence: 99%

“…So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. 3,4 On the other hand, various in silico tools have been explored for the prediction of the receptor-ligand binding affinity.…”

Section: Introductionmentioning

confidence: 99%

“…[18][19][20] In particular, improving selectivity for the human A 1 R subtype has been obtained by decorating the classical xanthine scaffold with 8-aryl or 8-cycloalkyl substituents. 21 On the other hand, hA 2B R selective antagonists have been developed with different substitutions at the N 1 , N 3 , and 8 positions in the same xanthine scaffold. [22][23][24] Regarding the pyrazolotriazolo-pyrimidine derivatives, position N 7 has been suggested to be crucial for the selectivity to the human A 2A R subtype.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

Michielan

Federico

Terfloth

et al. 2009

J. Chem. Inf. Model.

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Nowadays, in medicinal chemistry adenosine receptors represent some of the most studied targets, and there is growing interest on the different adenosine receptor (AR) subtypes. The AR subtypes selectivity is highly desired in the development of potent ligands to achieve the therapeutic success. So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. In the present study, we have carried out a novel application of the multilabel classification approach by combining our recently reported autocorrelated molecular descriptors encoding for the molecular electrostatic potential (autoMEP) with support vector machines (SVMs). Three valuable models, based on decreasing thresholds of potency, have been generated as in series quantitative sieves for the simultaneous prediction of the hA(1)R, hA(2A)R, hA(2B)R, and hA(3)R subtypes potency profile and selectivity of a large collection, more than 500, of known inverse agonists such as xanthine, pyrazolo-triazolo-pyrimidine, and triazolo-pyrimidine analogues. The robustness and reliability of our multilabel classification models were assessed by predicting an internal test set. Finally, we have applied our strategy to 13 newly synthesized pyrazolo-triazolo-pyrimidine derivatives inferring their full adenosine receptor potency spectrum and hAR subtypes selectivity profile.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

Michielan

Federico

Terfloth

et al. 2009

J. Chem. Inf. Model.

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“…For subtype selectivity regression model, we used SR directly as the dependent variable. For subtype selectivity discrimination model, compounds with SR greater than 1 or less than −1 were defined as selective agents2829. A SR equal to 1 indicates that the compound can bind to T1 with a potency 10-fold higher than to T2.…”

Section: Methodsmentioning

confidence: 99%

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

Kuang

et al. 2016

Sci Rep

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Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2B vs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2A vs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

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Selectivity-based QSAR approach for screening and evaluation of TRH analogs for TRH-R1 and TRH-R2 receptors subtypes

Cited by 2 publications

References 39 publications

Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

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