QSAR of adenosine receptor antagonists: Exploring physicochemical requirements for binding of pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine derivatives with human adenosine A3 receptor subtype

Kishore, D.; Balakumar, C.; Akkinepally, Raghuram Rao; Roy, Partha Pratim; Roy, Kunal

doi:10.1016/j.bmcl.2010.11.094

Cited by 44 publications

(10 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The calibration set was used for model generation. The prediction set was applied deal with overfitting of the network, whereas validation set which its molecules have no role in model building was used to investigate the predictive ability of the models for the external set [38,39].…”

Section: Model Validation and Statistical Parametersmentioning

confidence: 99%

Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

Shahpar¹,

Esmaeilpoor

2017

Chem. Method.

View full text Add to dashboard Cite

A quantitative structure-retention relationship (QSRR), was developed by using the genetic algorithm-partial least square (GA-PLS), Kernel partial least square (GA-KPLS) and LevenbergMarquardt artificial neural network (L-M ANN) approach for the prediction of the retention time (RT) of the doping agents in urine. The values of the retention time were obtained by using ultra-high-pressure liquid chromatography-quadrupole time-offlight mass spectrometry (UHPLC-QTOF-MS). A suitable set of the molecular descriptors was calculated and the important descriptors were selected by the aid of the GA-PLS and GA-KPLS. By comparing the results, GA-KPLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. This model was used to predict the RT values of some of doping agents which were not used in the modeling procedure. This is the first research on the QSRR of doping agents against the RT using the GA-PLS, GA-KPLS and L-M ANN model. KEYWORDS Graphical AbstractApproach to Chemometrics Models … P a g e | 106

show abstract

Section: Model Validation and Statistical Parametersmentioning

confidence: 99%

Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

Shahpar¹,

Esmaeilpoor

2017

Chem. Method.

View full text Add to dashboard Cite

show abstract

“…In this sense, a high value of the statistical characteristic (Q 2 > 0.5) is considered as proof of the high predictive ability of the model. However, this assumption is in many cases incorrect and can be that exist the lack of the correlation between the high LGO Q 2 and the high predictive ability of QSRR models has been established and corroborated recently [38]. Thus, the high value of LGO-CV Q 2 appears to be necessary but not sufficient condition for the models to have a high predictive power.…”

Section: Cross Validation Techniquementioning

confidence: 99%

Advanced QSRR Modeling of Organic Pollutants in Natural Water and Wastewater in Gas Chromatography Time-of-Flight Mass Spectrometry

Shahpar¹,

Esmaeilpoor

2017

Chem. Method.

View full text Add to dashboard Cite

“…Thus, it was our thought of interest to further investigate any changes in affinity and selectivity towards ARs for a novel series of triazolopyridine derivatives ( 4a–4t ) with 7‐amino (NH 2 ) and 6‐carbonitrile (CN) group, while varying the substitution pattern at the 2‐position of the scaffold with various aryl/hetero‐aryl moieties (Figure ). In continuation of our interest in the search of novel ligands for adenosine receptors (Balakumar et al., ; Chandrasekaran et al., ; Deb et al., ; Kaur et al., ; Kishore, Balakumar, Rao, Roy, & Roy, ; Prasad et al., ; Sirisha et al., ; Veeraswamy et al., ) and in the light of the above‐mentioned literature reports, some novel 5‐oxo[1,2,4]triazolo[1,5‐ a ]pyridine‐6‐carbonitrile derivatives ( 4a–4t ) have been synthesized as potential selective ligands for hA 1 AR.…”

Section: Introductionmentioning

confidence: 98%

7‐Amino‐2‐aryl/hetero‐aryl‐5‐oxo‐5,8‐dihydro[1,2,4]triazolo[1,5‐a]pyridine‐6‐carbonitriles: Synthesis and adenosine receptor binding studies

et al. 2019

View full text Add to dashboard Cite

A series of novel 7-amino-5-oxo-2-substituted-aryl/hetero-aryl-5, 8-dihydro[1,2,4] triazolo[1,5-a]pyridine-6-carbonitriles (4a-4t) was synthesized, characterized and evaluated for their binding affinity and selectivity towards hA 1 , hA 2A , hA 2B and hA 3 adenosine receptors (ARs). Compound 4a with a phenyl ring at 2-position of the triazolo moiety of the scaffold showed high affinity and selectivity for hA 1 AR (K i hA 1 = 0.076 μM, hA 2A = 25.6 μM and hA 3 > 100 μM). Introduction of various electron donating and withdrawing groups at different positions of the phenyl ring resulted in drastic reduction in affinity and selectivity towards all the ARs, except compound 4b with a 4-hydroxyphenyl group at 2-position. Interestingly, the replacement of the phenyl ring with a smaller heterocyclic thiophene ring (π excessive system) resulted in further improvement of affinity for hA 1 AR of compound 4t (K i hA 1 = 0.051 μM, hA 2A = 9.01 μM and hA 3 > 13.9 μM) while retaining the significant selectivity against all other AR subtypes similar to compound 4a. The encouraging results for compounds 4a and 4t indicate that substitution at 2-position of the scaffold with π-excessive systems other than thiophene may lead to even more potent and selective hA 1 AR antagonists. K E Y W O R D SA 1 adenosine receptors, adenosine receptor antagonists, cyanoacetic hydrazides, triazolopyridines

show abstract

QSAR of adenosine receptor antagonists: Exploring physicochemical requirements for binding of pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine derivatives with human adenosine A3 receptor subtype

Cited by 44 publications

References 66 publications

Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

Advanced QSRR Modeling of Organic Pollutants in Natural Water and Wastewater in Gas Chromatography Time-of-Flight Mass Spectrometry

7‐Amino‐2‐aryl/hetero‐aryl‐5‐oxo‐5,8‐dihydro[1,2,4]triazolo[1,5‐a]pyridine‐6‐carbonitriles: Synthesis and adenosine receptor binding studies

Contact Info

Product

Resources

About