Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis

Sokolović, Dušan; Stanković, Veroljub; Toskić, Dragan; Lilić, Ljubiša; Ranković, Goran; Ranković, Jasmina; Nedin-Rankovic, Gorana; Veselinović, Aleksandar M.

doi:10.1007/s11224-016-0776-z

Cited by 26 publications

(7 citation statements)

References 40 publications

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“…The obtained QSAR equations were of consistent statistical quality for sub‐training, calibration, test, and validation sets . In all the three equations, values of R 2 more than 0.7 indicated good fitted models and Q 2 more than 0.6 pointed toward good internal predictivity and stability of the developed models.…”

Section: Resultsmentioning

confidence: 66%

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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Section: Resultsmentioning

confidence: 66%

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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“…With the best-preferred values of T* and N*, the pIC 50 (endpoint) for each split was computed and the developed QSAR models are as the following: 2 demonstrate that all generated QSAR models from the statistical point of view are appropriate and match the requirements of various validation criteria. The robustness of established QSAR models was demonstrated by the numerical value of R 2 and Q 2 values which were more than 0.5 and 0.7 47,48 . In addition, the numerical value of the R 2 m metric for the validation set of all designed QSAR models was satisfactory and follows the criteria suggested by Roy et al 49 .…”

Section: Resultsmentioning

confidence: 99%

Quantitative structure–activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes

Hamzehali

Lotfi

Ahmadi

et al. 2022

Sci Rep

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Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R2, Q2, and IIC for the validation set of splits 1 to 3 are in the range of 0.7180–0.7755, 0.6891–0.7561, and 0.4431–0.8611 respectively. The numerical result of $${CR}_{p}^{2}$$ CR p 2 > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC50 are recognized and used for the mechanistic interpretation of structural attributes.

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“…A flowchart of a Monte Carlo optimization cycle is presented by Sokolovic et al [26]. At first cycle, the CW(x) of features is randomly generated and then optimized based on the proposed objective function.…”

Section: Descriptorsmentioning

confidence: 99%

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors

et al. 2023

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The 3C-like protease (3CLpro), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CLpro of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CLpro in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CLpro. The models were built using a newer objective function optimization of this software, known as the index of ideality correlation (IIC), which provides favorable results. The entire set of molecules was randomly divided into four sets including: active training, passive training, calibration and validation sets. The optimal descriptors were selected from the hybrid model by combining SMILES and hydrogen suppressed graph (HSG) based on the objective function. According to the model interpretation results, eight synthesized compounds were extracted and introduced from the ChEMBL database as good SARS CoV 3CLpro inhibitor. Also, the activity of the introduced molecules further was supported by docking studies using 3CLpro of both SARS-COV-1 and SARS-COV-2. Based on the results of ADMET and OPE study, compounds CHEMBL4458417 and CHEMBL4565907 both containing an indole scaffold with the positive values of drug-likeness and the highest drug-score can be introduced as selected leads.

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Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis

Cited by 26 publications

References 40 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

Quantitative structure–activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors

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