Investigation of 3D pharmacophore of N-benzyl benzamide molecules of melanogenesis inhibitors using a new descriptor Klopman index: uncertainties in model

Tokat, Tuğba Alp; Türkmenoğlu, Burçin; Güzel, Yahya; Kızılcan, Dilek Şeyma

doi:10.1007/s00894-019-4120-6

Cited by 6 publications

(7 citation statements)

References 54 publications

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“…According to these characteristics, the Klopman Index used in the interaction energy between L-R is a very comprehensive and powerful descriptor. In MCET [38,[40][41][42][43][44], we developed a new algorithm for KI that considers the total value of both Coulombic and covalent interactions.…”

Section: Principle and Methodsmentioning

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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Section: Principle and Methodsmentioning

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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“…Since the best of among different LRDs used in our previous studies (Guzel et al, 2018;Kizilcan et al, 2020;Tokat et al, 2019;Turkmenoglu and Guzel, 2018;Turkmenoglu et al, 2020; stands out as the Klopman index, was used as a descriptor type also in this study. The main feature of this descriptor is that it treats the interaction of hard acid and hard base together in an equation, not separately.…”

Section: A New Local Reactivity Descriptor: Klopman Indexmentioning

confidence: 99%

“…In MCET, a type of electronic property of atoms is selected, and the parameters of the receptor side belonging to the pharmacophore region are determined (Turkmenoglu et al, 2017;Yilmaz et al, 2011;Yilmaz et al, 2014). Among these, the "Klopman Index", which we have newly introduced to the literature, is a quite important descriptor (Guzel et al, 2018;Kizilcan et al, 2020;Tokat et al, 2019;Turkmenoglu and Guzel, 2018). The difference and superiority of this descriptor from others; it uses both atomic charge and frontier orbital (HOMO / LUMO) coefficients for each atom at the same time.…”

Section: Introductionmentioning

confidence: 99%

4D-QSAR Analysis of Some New Pyrrole Derivatives Using Klopman Index Descriptor

Kızılcan¹,

Türkmenoğlu

Güzel

et al. 2022

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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We applied the Klopman Index, the Local Reactive Descriptor (LRD), for 3-Dimensional (3D) interactions between the Ligand-Receptor (L-R), for some new pyrrole derivatives as antituberculosis agents for 4-Dimensional Quantitative Structure-Activity Relationship (4D-QSAR) investigations. The parameters of the receptor side belonging to the Pharmacophore (Pha), which consists of the interaction points suggested between L-R, were calculated in the Molecular Conformer Electron Topological (MCET) method we developed based on the LRD values on the ligand side. After the 4D-QSAR model was established with the Leave One Out Cross-Validation (LOO-CV) technique on the molecules in the training set, the model was confirmed on the molecules in the external test set. Statistical results obtained from both sets, Q 2 = 0.875 and R ext 2 = 0.918, respectively, were evaluated as satisfactory.

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“…Since thousands of descriptors can be generated, it is important to determine which feature makes it more important. To create a good model, a descriptor should associate some physicochemical/biological features with structural features, have minimal correlation with other descriptors, and be applicable to a wide class of molecules as a useful descriptor [5,6]. For this reason, even if the structural differences in the molecular skeleton are small, they should reflect this difference.…”

Section: Introductionmentioning

confidence: 99%

“…HOMO/LUMO frontier orbital coefficients (partial atomic charges derived from electron densities on atoms), Fukui index, and other electronic properties have been used alone or in combination with conventional descriptors [21][22][23][24]. According to frontier orbital theory, nucleophilic/electrophilic and hydrogen bond interactions can be explained according to energy maps in HOMO/ LUMO orbitals [6]. Nucleophilic attack occurs by electron flow from the HOMO-nucleophile to the LUMO-electrophile [25], while electrophilic attack occurs by hydrogen transfer from the hydrogen donor LUMO orbital to the hydrogen acceptor HOMO orbital [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the performance of different “local reactive descriptors” in 3D-QSAR analysis of enantioselective molecules

2021

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The interaction between Ligand-Receptor (L-R) to solve the pharmacological and toxicological problems of enantioselective molecules was studied as 3D Quantitative Structure-Activity Relationship (3D-QSAR). The biological activity of enantiomers of stereo isomeric molecules that differ in 3D can only be determined by the Local Reactive Descriptor (LRD). The parameters of the Pharmacophore (Pha) on the receptor side can be determined using LRDs of atoms of the molecule in 3D space. "Klopman index," which was brought to the literature as LRD by us, was compared with typical LRD descriptors such as atom charge, Fukui index, frontier orbital coefficients (HOMO/LUMO) in our Molecular Conformer Electron Topological (MCET) program. After predicting the 3D-QSAR model with the Leave One Out-Cross Validation (LOO-CV) technique on the molecules in the training set, the model was validated on the molecules in the external test set. Statistical results from both sets were evaluated for all LRDs, and the best results were obtained as Q 2 = 0.955 and R 2 = 0.964 in the Klopman index.

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Investigation of 3D pharmacophore of N-benzyl benzamide molecules of melanogenesis inhibitors using a new descriptor Klopman index: uncertainties in model

Cited by 6 publications

References 54 publications

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

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

4D-QSAR Analysis of Some New Pyrrole Derivatives Using Klopman Index Descriptor

Comparison of the performance of different “local reactive descriptors” in 3D-QSAR analysis of enantioselective molecules

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