Structure-based discovery of multitarget directed anti-inflammatory p-nitrophenyl hydrazones; molecular docking, drug-likeness, in-silico pharmacokinetics, and toxicity studies.

Babalola, Sodeeq; Igie, Nosakhare; Odeyemi, Isaiah

doi:10.26434/chemrxiv-2022-mrptw-v2

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The pharmacokinetic parameters are based on desirable adsorption, distribution, metabolism, excretion, and toxicity (ADMET) of the query drug when administered into the body (Ibrahim et al, 2020). An efficient and accurate ADMETlab 2.0 webserver (https://admetmesh.scbdd.com/) was utilized to predict numerous physicochemical, drug-likeness, pharmacokinetic, and toxicity parameters of molecules in the study (Babalola et al, 2022;Kar et al, 2022). In addition, the drug-likeness of the 5-benzyl-4-thiazolinones (48 molecules) was assessed based on Lipinski, Ghose, Veber, Egan, and Muegge rules using the SwissADME online webserver at http://www.swissadme.ch/index.php.…”

Section: Drug Likeness and Admet Prediction Studiesmentioning

confidence: 99%

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Abdullahi

Uzairu

Shallangwa

et al. 2022

Heliyon

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Section: Drug Likeness and Admet Prediction Studiesmentioning

confidence: 99%

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Abdullahi

Uzairu

Shallangwa

et al. 2022

Heliyon

View full text Add to dashboard Cite

“…scbdd. com/) was utilized to predict numerous physicochemical, drug-likeness, pharmacokinetic, and toxicity parameters of compounds in the study [35,36]. In addition, the drug-likeness of the compounds was assessed based on Lipinski, Ghose, Veber, Egan, and Muegge rules using the SwissADME online Web server at http:// www.…”

Section: Drug-likeness and Admet Prediction Studiesmentioning

confidence: 99%

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Abdullahi

Uzairu

Shallangwa

et al. 2022

Beni-Suef Univ J Basic Appl Sci

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Background Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2 = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2 = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2 = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2 = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents. Conclusion The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

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Structure-based discovery of multitarget directed anti-inflammatory p-nitrophenyl hydrazones; molecular docking, drug-likeness, in-silico pharmacokinetics, and toxicity studies.

Cited by 2 publications

References 21 publications

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

RETRACTED: In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

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