scholarly journals An automated workflow by using KNIME Analytical Platform: a case study for modelling and predicting HIV-1 protease inhibitors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ramtin Ranji ◽  
Chanat Thanavanich ◽  
Sri Devi Sukumaran ◽  
Sila Kittiwachana ◽  
Sharifuddin Md Zain ◽  
...  
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Optimal Number ◽  
Time Cost ◽  
Biological Behaviour ◽  
Structure Activity ◽  
Target Disease ◽  
Drugbank Database ◽  
Hiv 1

In this study, we have demonstrated an automated workflow by using KNIME Analytical Platform for modelling and predicting potential HIV-1 protease (HIVP) inhibitors. The workflow has been simplified in three easy steps i.e., 1) retrievethe database of inhibitors for the target disease from ChEMBL website and well-known drug from DrugBank database, 2) generate the descriptors and, 3) select the optimal number of features after machine learning models training. Our results have indicated that the random forest with auto prediction validation method is the most reliable with the best R2 value of 0.9394. Apparently, this workflow can be transformed easily for any other diseases and the quantitative structure-activity relationship (QSAR) model that has been developed can accurately predict in silico how chemical modifications might influence biological behaviour. Overall, the automated workflow which has been presented in this study may significantly reduce the time, cost and efforts needed to design or develop potential HIVP inhibitors.

Novel radial distribution function approach in the study of point mutations: the HIV-1 protease case study

2020 ◽  
Author(s):  
Jurica Novak ◽  
Maria A Grishina ◽  
Vladimir A Potemkin
Keyword(s):  
Distribution Function ◽  
Radial Distribution Function ◽  
Radial Distribution ◽  
Point Mutations ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Inhibition Constant ◽  
The One ◽  
Hiv 1

Background: Mutations are one of the engines of evolution. Under constant stress pressure, mutations can lead to the emergence of unwanted, drug resistant entities. Methodology: The radial distribution function weighted by the number of valence shell electrons is used to design quantitative structure–activity relationship (QSAR) model relating descriptors with the inhibition constant for a series of wild-type HIV-1 protease inhibitor complexes. The residuals of complexes with mutant HIV-1 protease were correlated with the energy of the highest occupied molecular orbitals of the residues introduced to enzyme via point mutations. Conclusion: Successful identification of residues Ile3, Asp25, Val32 and Ile50 as the one whose substitution influences the inhibition constant the most, demonstrates the potential of the proposed methodology for the study of the effects of point mutations.

Antimicrobial Screening, in Silico Studies and QSAR of Chalcone-based 1,4-disubstituted 1,2,3-triazole Hybrids

Drug Research ◽  
2020 ◽  
Author(s):  
Pinki Yadav ◽  
Kashmiri Lal ◽  
Ashwani Kumar
Keyword(s):  
Topoisomerase Ii ◽  
Antimicrobial Properties ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Binding Modes ◽  
E Coli ◽  
Structure Activity ◽  
In Silico Studies ◽  
Microbial Strains

AbstractThe in vitro antimicrobial properties of some chalcones (1a–1c ) and chalcone tethred 1,4-disubstituted 1,2,3-triazoles (2a–2u) towards different microbial strains viz. Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger and Candida albicans are reported. Compounds 2g and 2u exhibited better potency than the standard Fluconazole with MIC values of 0.0063 µmol/mL and 0.0068 µmol/mL, respectively. Furthermore, molecular docking was performed to investigate the binding modes of two potent compounds 2q and 2g with E. coli topoisomerase II DNA gyrase B and C. albicans lanosterol 14α-demethylase, respectively. Based on these results, a statistically significant quantitative structure activity relationship (QSAR) model was successfully summarized for antibacterial activity against B. subtilis.

scholarly journals Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Toshio Kasamatsu ◽  
Airi Kitazawa ◽  
Sumie Tajima ◽  
Masahiro Kaneko ◽  
Kei-ichi Sugiyama ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Ames Test ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Low Molecular Weight ◽  
Quantitative Structure ◽  
Industrial Chemicals ◽  
Structure Activity

Abstract Background Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure–activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. Results In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals’ Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model “StarDrop NIHS 834_67” showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. Conclusions A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

2021 ◽  
Vol 16 (10) ◽  
pp. 50-58
Author(s):  
Ali Qusay Khalid ◽  
Vasudeva Rao Avupati ◽  
Husniza Hussain ◽  
Tabarek Najeeb Zaidan
Keyword(s):  
Dengue Fever ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Contour Maps ◽  
Structure Activity ◽  
Bioactive Ligands

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.

Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis

RSC Advances ◽  
2015 ◽  
Vol 5 (70) ◽  
pp. 57030-57037 ◽  
Author(s):  
Arafeh Bigdeli ◽  
Mohammad Reza Hormozi-Nezhad ◽  
Hadi Parastar
Keyword(s):  
Gold Nanoparticles ◽  
Gold Nanoparticle ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Determining Factors

A nano-quantitative structure-activity relationship (nano-QSAR) model is proposed to indicate the determining factors responsible in the exocytosis of gold nanoparticles in macrophages.

scholarly journals Conceptual Framework for the Conservation of Natural Environment from Toxic Ionic Liquids by QSAR Model

2021 ◽  
Vol 287 ◽  
pp. 03007
Author(s):  
Muhammad Ishaq Khan ◽  
Dzulkarnain Zaini ◽  
Azmi Mohd Shariff
Keyword(s):  
Ionic Liquids ◽  
Natural Environment ◽  
Daily Life ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Good Choice ◽  
Ionic Structure ◽  
Quantitative Structure ◽  
Imidazolium Ionic Liquids ◽  
Structure Activity

The natural environment has been affected by human activities to fulfil daily life needs. Abundance and hazardousness of the chemicals including ionic liquids is one of the most challenging aspect to be handled by human as well as for the natural environment. Due to ionic structure, ionic liquids are very good choice for a variety of applications. The natural environment might be affected by the ionic liquids which can be toxic. Therefore, there is a need to address this problem by studying the ecotoxicological behaviour of these ionic liquids. The main objective of current research is to model the toxicity ecotoxicological behaviour is studied by quantitative structure activity relationship (QSAR). QSARs predicts the toxicity of ionic liquids. In current research a relationship between polarizability and toxicity for imidazolium ionic liquids with different alky chain length having NTF2 anion has been modelled. The success of current research will be very helpful to protect the nature by minimizing the killing of testing animals as well as ensuring the safety of biotic components of the ecosystem.

scholarly journals Quantitative Structure–Activity Relationships for the Flavonoid-Mediated Inhibition of P-Glycoprotein in KB/MDR1 Cells

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1661 ◽  
Author(s):  
Mengmeng Xia ◽  
Yajing Fang ◽  
Weiwei Cao ◽  
Fuqiang Liang ◽  
Siyi Pan ◽  
...  
Keyword(s):  
Inhibitory Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Glycoprotein P ◽  
Chemotherapy Drugs ◽  
Structure Activity ◽  
P Glycoprotein ◽  
Daily Diet ◽  
Low Toxicity

P-glycoprotein (P-gp) serves as a therapeutic target for the development of inhibitors to overcome multidrug resistance (MDR) in cancer cells. In order to enhance the uptake of chemotherapy drugs, larger amounts of P-gp inhibitors are required. Besides several chemically synthesized P-gp inhibitors, flavonoids as P-gp inhibitors are being investigated, with their advantages including abundance in our daily diet and a low toxicity. The cytotoxicity of daunorubicin (as a substrate of P-gp) to KB/MDR1 cells and the parental KB cells was measured in the presence or absence of flavonoids. A two-dimensional quantitative structure–activity relationship (2D-QSAR) model was built with a high cross-validation coefficient (Q2) value of 0.829. Descriptors including vsurf_DW23, E_sol, Dipole and vsurf_G were determined to be related to the inhibitory activity of flavonoids. The lack of 2,3-double bond, 3′-OH, 4′-OH and the increased number of methoxylated substitutions were shown to be beneficial for the inhibition of P-gp. These results are important for the screening of flavonoids for inhibitory activity on P-gp.

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