Pharmacophore and Functional Group Identification of 4,4'-dihydroxydiphenylmethane as Bisphenol-A (BSA) Derivative

Yilmaz, Hayriye; Boz, Mehmet Akif; Türkmenoğlu, Burçin; Güzel, Yahya

doi:10.4314/tjpr.v13i1.17

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…This method based on the mechanism of evaluation of species, in which the higher descriptor weights are the more preserved in the mathematic model, while the lower weight is eliminated. In this form, the best model which represents the experimental biological activity isobtained [ 36 , 37 , 38 , 40 , 41 ]. We selected the resulting model in the range of 1–5 variables per model by limiting the GA variable selection algorithm.…”

Section: Methodsmentioning

confidence: 99%

Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

2015

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The knowledge of physico-chemical properties of carbon nanotubes, including behavior in organic solvents is very important for design, manufacturing and utilizing of their counterparts with improved properties. In the present study a quantitative structure-activity/property relationship (QSAR/QSPR) approach was applied to predict the dispersibility of single walled carbon nanotubes (SWNTs) in various organic solvents. A number of additive descriptors and quantum-chemical descriptors were calculated and utilized to build QSAR models. The best predictability is shown by a 4-variable model. The model showed statistically good results (R2training = 0.797, Q2 = 0.665, R2test = 0.807), with high internal and external correlation coefficients. Presence of the X0Av descriptor and its negative term suggest that small size solvents have better SWCNTs solubility. Mass weighted descriptor ATS6m also indicates that heavier solvents (and small in size) most probably are better solvents for SWCNTs. The presence of the Dipole Z descriptor indicates that higher polarizability of the solvent molecule increases the solubility. The developed model and contributed descriptors can help to understand the mechanism of the dispersion process and predictorganic solvents that improve the dispersibility of SWNTs.

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Section: Methodsmentioning

confidence: 99%

Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

2015

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“…15, 16 Multiple complementary applications of 4D-QSAR paradigm 17 may be a good way to extend our knowledge and understanding of the SARs of flavonoids using this "quality for quantity" argument. The fourth "dimension" of the 4D-QSAR paradigm is ensemble sampling of the spatial features of the members of the training set.…”

Section: 3mentioning

confidence: 99%

“…The MolFiles databases were transformed to Electron Topological Matrix (ETM) format using ETM Programmer (ETMP). 16…”

Section: Quantum Chemical Calculationsmentioning

confidence: 99%

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4D-QSAR study of flavonoid derivatives with MCET method

Türkmenoğlu

Yilmaz²,

Su³

et al. 2017

International Journal of Chemistry and Technology

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The Molecular Conformer Electron Topological (MCET) method was performed for the identification of the pharmacophore (Pha) group and predicting inhibitory activity of 42 flavonoid ligands on gamma-aminobutyric acid/benzodiazepine receptor complex (GABAA/BZR). In this method, Electron Topological Matrix (ETM) was used to visualize 3D structural descriptors. Multiple comparisons of ETM matrices for all flavonoid compounds allow us to define Pha-structure. Genetic algorithm (GA)-Partial Least-Squares (PLS) methods were performed to construct QSAR model and to select most important descriptors of the training set (32 compounds) and test set (10 compounds). The GA-PLS based model showed good results, q 2 = 0.808 and r 2 test = 0.775 with high internal and external validation. The developed model can help to understand the inhibitory mechanism.

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“…Molecular conformers were determined by using MMFF with Spartan'10 program and quantum chemical calculations were made with Hartree-Fock 6-31G*. For each conformer to be used in MCET method, '*.txt' les produced from Spartan were converted to "Electron Topological Matrix" (ETM) les with ETM-Program (ETM-P)[37][38][39][40].…”

mentioning

confidence: 99%

Optimal Division of Molecules Into Training And Test Sets With A New Tool To Predict Pharmacophore In 3D-QSAR

Tokat

Türkmenoğlu

Güzel

2021

Preprint

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According to the descriptors in the pharmacophore model, dividing molecules into training and test sets serves to create a good model. It is difficult to track the Local Reactive Descriptor (LRD) effect of the pharmacophore at each interaction point in the 3D metric system. A subset of clusters of atoms can correspond to all or part of the pharmacophore structure. In this study, the multidimensional system of the subset was reduced to a one-dimensional index and the Vector Fingerprint Functions (VFF) of the molecules were created. Models were established by dividing molecules with close and similar VFFs into training and test sets. Sub-clusters were examined for all molecules by applying the Genetic Algorithm (GA). The model was predicted using the Leave One Out-Cross Validation (LOO-CV) method and verified with an external test set. The statistical results of the model obtained according to the division in the new method we developed (Q2 = 0.604 and R2 = 0.760 for training-80 and external test-20 sets, respectively) were compared with random and manual division results.

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Pharmacophore and Functional Group Identification of 4,4'-dihydroxydiphenylmethane as Bisphenol-A (BSA) Derivative

Cited by 12 publications

References 22 publications

Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach

4D-QSAR study of flavonoid derivatives with MCET method

Optimal Division of Molecules Into Training And Test Sets With A New Tool To Predict Pharmacophore In 3D-QSAR

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