Synthesis and Quantitative Structure‐activity Relationships Study for Arylpropenamide Derivatives as Inhibitors of Hepatitis B Virus Replication

Ma, Mingliang; Jiang, Xingjun; Wang, Xue-Ding; Zhang, Hao; Yang, Weiqing; Zhang, Yuanyuan; Peng, Changrong; Li, Zicheng; Yang, Jing; Du, Qiang; Ma, Menglin

doi:10.1111/cbdd.12774

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…In quantum mechanics, DFT (density functional theory) calculation is an estimation of the molecular electronic features such as electron density, frontier molecular orbital (HOMO and LUMO) density and electrostatic molecular map in order to predict the reactivity of proposed inhibitors [75,76]. Geometry optimization of compound was performed using hybrid DFT approach at B3LYP (Becke's three-parameter exchange potential and the Lee-Yang-Parr correlation functional) with 6-31G** basis set [77,78].…”

Section: Dft Calculationmentioning

confidence: 99%

In silico characterization of the NiRAN domain of RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV2

Dwivedy¹,

Mariadasse²,

Ahmed³

et al. 2020

Preprint

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Section: Dft Calculationmentioning

confidence: 99%

In silico characterization of the NiRAN domain of RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV2

Dwivedy¹,

Mariadasse²,

Ahmed³

et al. 2020

Preprint

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“…Several models have been designed for ARV therapies. − Almost all models and studies are designed specifically for HIV ARV drugs and just a few for HBV. We have not found ARV ML models for the rest of the retroviruses included in this study.…”

Section: Resultsmentioning

confidence: 99%

“…Our model was developed including all available experimental preclinical assays in order to cover new molecules including more information on the assays. Finally, the HBV work goal was to achieve the synthesis and analysis of arylpropenamide derivatives, that work as inhibitors of HBV replication, focusing exclusively on thermal energy and lower entropy as input variables, leaving out important information for HBV possible candidates. The last listed work carried out a comparative study of 6 different ML techniques in HIV, including deep neural networks .…”

Section: Resultsmentioning

confidence: 99%

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

et al. 2019

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Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c 0 ), 55 protein accessions (c 1 ), 83 cell lines (c 2 ), 64 organisms of assay (c 3 ), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

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“…In quantum mechanics, DFT (density functional theory) calculation is an estimation of the molecular electronic features such as electron density, frontier molecular orbital (HOMO and LUMO) density and electrostatic molecular map in order to predict the reactivity of proposed inhibitors [83,84]. Geometry optimization of compound was performed using hybrid DFT approach at B3LYP (Becke’s three-parameter exchange potential and the Lee–Yang–Parr correlation functional) with 6-31G ** basis set [85,86].…”

Section: Methodsmentioning

confidence: 99%

Characterization of the NiRAN domain from RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV-2

Dwivedy

Mariadasse

Ahmad

et al. 2021

Preprint

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Apart from the canonical fingers, palm and thumb domains, the RNA dependent RNA polymerases (RdRp) from the viral order Nidovirales possess two additional domains. Of these, the function of the Nidovirus RdRp associated nucleotidyl transferase domain (NiRAN) remains unanswered. The elucidation of the 3D structure of RdRp from the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), provided the first ever insights into the domain organisation and possible functional characteristics of the NiRAN domain. Using in silico tools, we predict that the NiRAN domain assumes a kinase or phosphotransferase like fold and binds nucleoside triphosphates at its proposed active site. Additionally, using molecular docking we have predicted the binding of three widely used kinase inhibitors and five well characterized anti-microbial compounds at the NiRAN domain active site along with their drug-likeliness as well as DFT properties. For the first time ever, using basic biochemical tools, this study shows the presence of a kinase like activity exhibited by the SARS-CoV-2 RdRp. Interestingly, the proposed kinase inhibitors and a few of the predicted nucleotidyl transferase inhibitors significantly inhibited the aforementioned enzymatic activity. In line with the current global COVID-19 pandemic urgency and the emergence of newer strains with significantly higher infectivity, this study provides a new anti-SARS-CoV-2 drug target and potential lead compounds for drug repurposing against SARS-CoV-2.

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Synthesis and Quantitative Structure‐activity Relationships Study for Arylpropenamide Derivatives as Inhibitors of Hepatitis B Virus Replication

Cited by 11 publications

References 24 publications

In silico characterization of the NiRAN domain of RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV2

In silico characterization of the NiRAN domain of RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV2

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Characterization of the NiRAN domain from RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV-2

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