Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles

Türkmenoğlu, Burçin; Güzel, Yahya

doi:10.1016/j.compbiolchem.2018.07.003

Cited by 16 publications

(12 citation statements)

References 43 publications

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“…If the coordinate values of the atoms in the reference molecules are like those in the template, it does not create a new eld, but if it is different, there may be the beginning of a new cluster eld. However, when a cluster resource does not have enough atoms within its tolerance limits, it cannot lead to clustering 38 . Since an atom from each molecule can be placed in a cluster, clusters that do not contain enough atoms are neglected.…”

Section: Principle and Methodsmentioning

confidence: 99%

“…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%

“…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%

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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%

Section: Principle and Methodsmentioning

confidence: 99%

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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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“…The detailed description of successful applications of RI/RD-4D-QSAR paradigm is beyond the scope of this paper and it can be found elsewhere [ 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 ]. The rough characterization of the scientific projects, where 4D-QSAR methodology was implemented with the specification of the applied protocol, objects of interests and references is reported in Table 1 .…”

Section: 4d-qsar Dialects: Towards ‘Magic Bullet’mentioning

confidence: 99%

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

Bąk

2021

IJMS

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A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

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“…Molecular descriptors are logical and mathematical results that represent the chemical information of the molecule symbolically and numerically and fall into two main categories. Primary descriptors: While they are classified as 0D, 1D, 2D, 3D, and 4D [11][12][13][14][15][16][17] secondary descriptors; Log P is classified as general physicochemical properties such as molar refraction, dipole moment and polarization, and molecular weight [18][19][20]. As the degree of effectiveness of the descriptors from 0D to 4D increases, the degeneration decreases and may approach zero.…”

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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Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles

Cited by 16 publications

References 43 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

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

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

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