Highly biodegradable fluoroquinolone derivatives designed using the 3D-QSAR model and biodegradation pathways analysis

2020 ◽  
Vol 191 ◽  
pp. 110186 ◽  
Author(s):  
Yilin Hou ◽  
Yuanyuan Zhao ◽  
Qing Li ◽  
Yu Li
2012 ◽  
Vol 8 (3) ◽  
pp. 436-451 ◽  
Author(s):  
Pradeep Hanumanthappa ◽  
Mahesh K. Teli ◽  
Rajanikant G. Krishnamurthy
Keyword(s):  
3D Qsar ◽  

2021 ◽  
Vol 16 (10) ◽  
pp. 50-58
Author(s):  
Ali Qusay Khalid ◽  
Vasudeva Rao Avupati ◽  
Husniza Hussain ◽  
Tabarek Najeeb Zaidan

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.


Author(s):  
Avineesh Singh ◽  
Harish Rajak

Objective: Histone deacetylase inhibitors (HDACi) have four essential pharmacophores as cap group, connecting unit, a linker moiety and zinc binding group for their anticancer and histone deacetylase (HDAC) inhibition activity. On the basis of this fact, the objective of this research was to evaluate the exact role of pyrazole nucleus as connecting unit and its role in the development of newer HDACi.Methods: Ligand and structure-based computer-aided drug design strategies such as pharmacophore and atom based 3D QSAR modelling, molecular docking and energetic based pharmacophore mapping have been frequently applied to design newer analogs in a precise manner. Herein, we have applied these combinatorial approaches to develop the structure-activity correlation among novel pyrazole-based derivatives.Results: the Pharmacophore-based 3D-QSAR model was developed employing Phase module and e-pharmacophore on compound 1. This 3D-QSAR model provides fruitful information regarding favourable and unfavourable substitution on pyrazole-based analogs for HDAC1 inhibition activity. Molecular docking studies indicated that all the pyrazole derivatives bind with HDAC1 proteins and showed critical hydrophobic interaction with 5ICN and 4BKX HDAC1 proteins.Conclusion: The outcome of the present research work clearly indicated that pyrazole nucleus added an essential hydrophobic feature in cap group and could be employed to design the ligand molecules more accurately.


RSC Advances ◽  
2016 ◽  
Vol 6 (113) ◽  
pp. 112704-112711 ◽  
Author(s):  
Yu-Jie Zhu ◽  
Xiao-Feng Guo ◽  
Zhi-Jin Fan ◽  
Lai Chen ◽  
Liu-Yong Ma ◽  
...  

Insecticidal and fungicidal active thiazole-containing tetrahydropyridine derivatives with accurately predicted 3D QSAR model againstAphis LaburniKaltenbach and predicted potential anti-fungus target of fumarate reductase without cross resistance were synthesized.


Data in Brief ◽  
2019 ◽  
Vol 22 ◽  
pp. 471-483 ◽  
Author(s):  
Giuseppe Floresta ◽  
Agostino Cilibrizzi ◽  
Vincenzo Abbate ◽  
Ambra Spampinato ◽  
Chiara Zagni ◽  
...  
Keyword(s):  
3D Qsar ◽  

Author(s):  
Prasanthi Polamreddy ◽  
Vinita Vishwakarma ◽  
Manoj Kumar Mahto

Objective: The objective of the current study was to elucidate the 3D pharmacophoric features of benzothiadiazine derivatives that are crucial for inhibiting Hepatitis C virus (HCV) Non-structural protein 5B (NS5B) and quantifying the features by building an atom based 3D quantitative structure-activity relationship (3D QSAR) model.Methods: Generation of QSAR model was carried out using PHASE 3.3.Results: A five-point pharmacophore model with two hydrogen bond acceptors, one negative ionization potential and two aromatic rings (AANRR) was found to be common among a maximum number of benzothiadiazine based NS5B inhibitors. A statistically significant 3D QSAR model was obtained from AANRR.6 which had correlation-coefficient (R2) value of 0.924, cross-validated correlation-coefficient (Q2) of 0.774, high Fisher ratio of 138 and low root mean square standard error (RMSE=0.29). There is another parameter, Pearson’s R, its value emphasizes correlation between predicted and observed activities of the test set. For the current model, Pearson’s R-value is 0.90, hence underlining the good quality of the model. The present study suggests that nitrogen atom of benzothiadiazine sulfamide ring, oxyacetamide group attached to C7 carbon of benzothiadiazine and sulfonamide oxygens are crucial for NS5B inhibitory activity. Prediction of activities of hit drugs generated in earlier research suggests that Aprepitant (Phase predicted activity: 6.9) could be a potential NS5B inhibitor.Conclusion: This 3D QSAR model developed was statistically good and can be used to predict the activities of newly designed NS5B inhibitors and virtual screening as well. Predict the activities of newly designed NS5B inhibitors and virtual screening as well.


2020 ◽  
Vol 76 (9) ◽  
pp. 3188-3198
Author(s):  
Jixiang Chen ◽  
Yuqin Luo ◽  
Chengqian Wei ◽  
Sikai Wu ◽  
Rong Wu ◽  
...  

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