3D-QSAR and molecular docking studies of amino-pyrimidine derivatives as PknB inhibitors

Damre, Mangesh V.; Gangwal, Rahul P.; Dhoke, Gaurao V.; Lalit, Manisha; Sharma, Dipna; Khandelwal, Kanchan; Sangamwar, Abhay T.

doi:10.1016/j.jtice.2013.05.016

Cited by 12 publications

(14 citation statements)

References 21 publications

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“…Threedimensional structures were built using the SKETCH option in SYBYL. All compounds were minimized under the Tripos standard force field, [5] with Gasteiger‐Hückel atomic partial charges by the Powell method with a convergence criterion of 0.01 Kcal/mol Å [6] …”

Section: Resultsmentioning

confidence: 99%

Computational and Retrosynthetic Investigation of Isoxazole‐Bearing Chalcones as Antioxidant Activate Compounds

Moukhliss

Koubi

Alaqarbeh³

et al. 2023

ChemistrySelect

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Peroxiredoxin 5 (1HD2) is an antioxidant enzyme that catalyzes the decrease of peroxide, and act as a regulator of Redox signaling. It is a potential target for working on new antioxidants. 3D-QSAR method was applied to a set of isoxazoles by using the comparative analysis of molecular fields (CoMFA and CoMSIA). Five new drug candidates (Pr1-Pr5) have been proposed. The study of the interactions between the drug candidate and antioxidant receptor 1HD2 was done by molecular docking. The results of the ADME (Absorption, Distribution, Metabolism, and Excretion) showed that these drug candidates are orally bioavailable and have high gastrointestinal absorption and good permeability. Molecular dynamics was used at 100 ns to study the stability of the MA-1HD2 and Pr5-1HD2 complexes obtained after molecular docking. Using the retrosynthesis approach, we have proposed a pathway for synthesizing the drug candidates. This study provides useful information on the antioxidant activity of isoxazolebased compounds.

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Section: Resultsmentioning

confidence: 99%

Computational and Retrosynthetic Investigation of Isoxazole‐Bearing Chalcones as Antioxidant Activate Compounds

Moukhliss

Koubi

Alaqarbeh³

et al. 2023

ChemistrySelect

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“…In this study, we use a proposal adapted from Damre et al . [20] that takes into account structural diversity and biological activity values.…”

Section: Methodsmentioning

confidence: 99%

Developing QSAR Models for the Identification of Inhibitors TargetingMycobacterium tuberculosisEnoyl-ACP Reductase Enzyme

Karolczak

Timmers

Caceres

2023

Preprint

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Tuberculosis is a matter of global concern due to its prevalence in developing countries and the ability of mycobacterium to present resistance to existing therapeutic regimens. In this context, the present project proposes the QSAR (Quantitative Structure-Activity Relationships) modeling, as a way of identifying and evaluating, in silico, the estimated inhibitory activity of candidate molecules for the molecular improvement stages and/or in vitro assays, reducing research time and financial costs. For this purpose, the SAR (Structure-Activity Relationships) study conducted by He, Alian and Montellano (2007) was used on a series of arylamides, tested as inhibitors of the enzyme enoyl-ACP-reductase (InhA) of Mycobacterium tuberculosis. The Hansh-Fujita (classical) and CoMFA (Comparative Molecular Field Analysis) QSAR models were developed. The classic QSAR model obtained the best statistical result, using Multiple Linear Regression (MLR), with internal validation with correlation factor R2 = 0.9012 and predictive quality, according to the Stone-Geisser indicator Q2 = 0.8612. In the external validation, a correlation factor R2 = 0.9298 and a Q2= 0.720 was obtained, indicating a highly predictive mathematical model. The CoMFA Model managed to obtain a Q2 = 0.6520 in the internal validation, which allowed the energy fields around the molecules used to be estimated, this is essential information to foster molecular improvement. A library of small molecules was built, with analogs to those used in the SAR study, which were subjected to classic QSAR function, resulting in a group of ten molecules with high estimated biological activity. The molecular docking results suggest that the ten analogs identified by the classical QSAR model presented favorable estimated free energy of binding. The conclusion points to the QSAR methodology as an efficient and effective tool in the search and identification of promising drug-like molecules.

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“…Based on the analysis method described above, the lowest energy conformation of each molecule was determined for the definitive QSAR studies. Molecular alignment is one of the most essential steps for the generation of the best CoMFA and CoMSIA models [ 21 ]. Thus, molecular alignment was performed using the Distill alignment technique, a user-defined common core of the Sybyl tools [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Molecular Modeling Study for the Design of Novel Peroxisome Proliferator-Activated Receptor Gamma Agonists Using 3D-QSAR and Molecular Docking

Jian

Yang

et al. 2018

IJMS

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Type 2 diabetes is becoming a global pandemic disease. As an important target for the generation and development of diabetes mellitus, peroxisome proliferator-activated receptor γ (PPARγ) has been widely studied. PPARγ agonists have been designed as potential anti-diabetic agents. The advanced development of PPARγ agonists represents a valuable research tool for diabetes therapy. To explore the structural requirements of PPARγ agonists, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking studies were performed on a series of N-benzylbenzamide derivatives employing comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and surflex-dock techniques. The generated models of CoMFA and CoMSIA exhibited a high cross-validation coefficient (q2) of 0.75 and 0.551, and a non-cross-validation coefficient (r2) of 0.958 and 0.912, respectively. The predictive ability of the models was validated using external validation with predictive factor (r2pred) of 0.722 and 0.682, respectively. These results indicate that the model has high statistical reliability and good predictive power. The probable binding modes of the best active compounds with PPARγ active site were analyzed, and the residues His323, Tyr473, Ser289 and Ser342 were found to have hydrogen bond interactions. Based on the analysis of molecular docking results, and the 3D contour maps generated from CoMFA and CoMSIA models, the key structural features of PPARγ agonists responsible for biological activity could be determined, and several new molecules, with potentially higher predicted activity, were designed thereafter. This work may provide valuable information in further optimization of N-benzylbenzamide derivatives as PPARγ agonists.

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3D-QSAR and molecular docking studies of amino-pyrimidine derivatives as PknB inhibitors

Cited by 12 publications

References 21 publications

Computational and Retrosynthetic Investigation of Isoxazole‐Bearing Chalcones as Antioxidant Activate Compounds

Computational and Retrosynthetic Investigation of Isoxazole‐Bearing Chalcones as Antioxidant Activate Compounds

Developing QSAR Models for the Identification of Inhibitors TargetingMycobacterium tuberculosisEnoyl-ACP Reductase Enzyme

Molecular Modeling Study for the Design of Novel Peroxisome Proliferator-Activated Receptor Gamma Agonists Using 3D-QSAR and Molecular Docking

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