Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors

Živković, Jelena; Trutić, Nataša; Veselinović, Jovana B.; Nikolić, Gordana; Veselinović, Aleksandar M.

doi:10.1016/j.compbiomed.2015.07.004

Cited by 22 publications

(12 citation statements)

References 54 publications

(79 reference statements)

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“…Optimal descriptor (DCW) used for constructing QSAR models, based on the chosen SMILES notation, was calculated according to the following equation:

D C W true(normalS normalM normalI normalL normalE normalS, normalT, N_{normale normalp normalo normalc normalh} true) = α Σ C W true(S_{k} true) + β normalΣ C W true({S S}_{k} true) + x . C W true(normalN normalO normalS normalP true)

where α and β are 1 or 0; CW is the correlation weight for a structural attribute extracted from SMILES; S k and SS k are representation of molecular fragments of SMILES where S k relate to one or two symbols from SMILES that cannot be examined separately; SS k is the permutation of two S k in SMILES; NOSP is a global molecular descriptor which reflects the presence or absence of nitrogen, oxygen, sulphur, and phosphorus. Threshold ( T *) and number of epochs, N epoch (N*), are mathematical functions for the correlation between experimental and calculated values of the endpoint for sub‐training, calibration, and test sets .…”

Section: Methodsmentioning

confidence: 99%

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Kumar

Chauhan²

2016

Archiv der Pharmazie

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SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R = 0.8350, Q = 0.7491 for the test set and R = 0.9655, Q = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes.

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“…Optimal descriptor (DCW) used for constructing QSAR models, based on the chosen SMILES notation, was calculated according to the following equation:

D C W true(normalS normalM normalI normalL normalE normalS, normalT, N_{normale normalp normalo normalc normalh} true) = α Σ C W true(S_{k} true) + β normalΣ C W true({S S}_{k} true) + x . C W true(normalN normalO normalS normalP true)

Section: Methodsmentioning

confidence: 99%

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Kumar

Chauhan²

2016

Archiv der Pharmazie

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“…The experimental or calculated properties obtained from chemical structures are the descriptors which characterize specific information of the molecule being studied. The QSAR models provide insights into the essential structural information of the ligands or inhibitors which contribute to biological activity [22]. Descriptors are generally categorised as physico-chemical, topological and electronic, geometric and structural, and simple indicatorparameters.…”

Section: Introductionmentioning

confidence: 99%

“…In general the QSAR models are developed on molecular graph based descriptors [26][27][28] but the simplified molecular input-line entry system (SMILES) representation can also be considered for the molecular structure [29][30][31] which can further be used for molecular descriptor calculations followed by development of QSAR models. The descriptors based on SMILES notation depend both on the molecular structure and the property under analysis irrespective of details from the 3D-molecular geometry [22].…”

Section: Introductionmentioning

confidence: 99%

Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors

Islam

Pillay

2016

Chemometrics and Intelligent Laboratory Systems

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Highlights• QSAR models were developed on HIV-protease inhibitors.• Descriptors were generated from SMILES-based structures.• Monte Carlo algorithm was used to correlate the biological activity with descriptors.• Models were developed with-and without considering influence of inhibitory activity in chemical structures.• Presence of rings in the molecules was found to be important for inhibition of HIVprotease. AbstractSimplified Molecular Input Line Entry System (SMILES) descriptor based quantitative structure-activity relationship (QSAR) study was performed on a set of HIV-protease inhibitors to explore the structural functionalities for inhibition of the HIV-protease. For this purpose a set of HIV-inhibitors was collected from the literature along with their inhibitory constants. Monte Carlo optimization-based CORAL software was used for QSAR model development. Firstly, the dataset was divided into three random splits and secondly each split was divided into training, calibration, test and validation sets. Training set was used for model development whereas the rest of the sets was used to assess the quality of the developed models. QSAR models were developed with and without considering the influence of cyclic rings towards the inhibitory activity. Statistical quality of QSAR models developed from all splits were very good and fulfilled the criteria. explained that selected models are robust in nature and efficient enough to predict the inhibitory activity of the molecules outside of the training set. Statistical parameters also suggested that the presence of cyclic rings have a crucial impact on inhibitory activity. The 2 molecular fragments were found to be important for the increase or decrease of the inhibitory activity which explained that models have mechanistic interpretation. This ligand-based QSAR study can provide clear directions to design and modulate potential HIV-protease inhibitors.

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“…5 However, statutory of discovering new lead compounds which having the antagonistic activity against diabetes is essential. The use of quantitative structure-activity relationships (QSAR), since their advent in 1962, has become increasingly helpful in understanding many aspects of chemical-biological interactions in drug and other scientific research.…”

mentioning

confidence: 99%

“…Resistance to the biological actions of insulin in tissues like muscle, liver, and adipocytes is a major feature of the pathophysiology in type 2 diabetes. 5 Glycogen synthase kinase (GSK) is a multi-targeted serine/threonine kinase, originally identified as an enzyme, having two identical isoforms namely GSK-3α (51kDa) and GSK-3β (47kDa).…”

mentioning

confidence: 99%

Untitled

2016

IJPSR

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ABSTRACT:The best QSAR model were generated with left of adept and significant descriptors like electronic, lipophilic and topological, using multiple linear regression (MLR) and partial least square (PLS), model further explained by using forward feed neural network analysis (FFNN). QSAR is a kind of technique that directly correlates in between chemical structure to their biological activity. The best MLR statistical expressions were evaluated with good predictive and authenticated ability and the values were S =0.367, F =53.06 r =0.910, r² =0.828, r 2 (cv) =0.780. The r 2 (training and test-set) values of MLR, PLS and FFNN are 0.82, 0.71, 0.82, 0.71 and 0. 81, 0.74 respectively, which predicts the soundness of the model. The model reveals that total dipole moment, bond lipole and kappa 3 are prerequisite descriptors for determining further promising GSK-3β antagonist with high and liable potency against target. In addition to QSAR modelling, Lipinski's rule of five was employed on a series and we found no violation in it, which means 3-aryl-4-(arylhydrazono) 1H pyrazol-5-ones has enough good pharmacokinetic profile, and it become more accentuated when orally active anti-diabetic agents will formed.

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Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors

Cited by 22 publications

References 54 publications

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Use of the Monte Carlo Method for OECD Principles‐Guided QSAR Modeling of SIRT1 Inhibitors

Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors

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