<i>In silico</i> drug discovery of IKK-β inhibitors from 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives based on QSAR, docking, molecular dynamics and drug-likeness evaluation studies

Hammoudi, Nour-El-Houda; Benguerba, Yacine; Attoui, Ayoub; Hognon, Cécilia; Lemaoui, Tarek; Sobhi, Widad; Benaicha, Mohamed; Badawi, Michaël; Monari, Antonio

doi:10.1080/07391102.2020.1819878

Cited by 22 publications

(7 citation statements)

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“…To strengthen the validity of a QSPR model, the Organization for Economic Cooperation and Development (OECD) suggested the use of the applicability domain (AD) as a validation tool to estimate the uncertainties in predicting new compounds by a proposed QSPR model. , The AD is a theoretical region in the chemical space, which represents the limitation of a model . The model is considered to be valid and able to predict new molecules if the training and testing data sets are within the domain of applicability of that model .…”

Section: Resultsmentioning

confidence: 99%

“…In the William plot, the applicability domain boundaries range between −3 < SDR < + 3 and 0 < h i < h *. The standardized residuals can be calculated by where y exp and y pred are the experimentally determined and the model predicted pH values, respectively. The coverage of the AD structural range could be evaluated based on the number of ESs within the AD compared to the outliers in a William plot by where p inside represents the points contained inside the domain, while p total represents the total number of data points.…”

Section: Methodsmentioning

confidence: 99%

“…In the William plot, the applicability domain boundaries range between −3 < SDR < + 3 and 0 < h i < h*. The standardized residuals can be calculated by 91 y y SDR where p inside represents the points contained inside the domain, while p total represents the total number of data points.…”

Section: Model Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Lemaoui

Hatab

Darwish

et al. 2021

ACS Sustainable Chem. Eng.

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The case of sustainable solvents is of great interest both academically and industrially. With research communities becoming more aware of the negative impacts of conventional organic solvents, a range of greener and more sustainable solvents have been developed to counter the harmful drawbacks associated with conventional solvents. Among these, eutectic solvents (ESs) attracted considerable attention for their "green" properties and have proven their usefulness as environmentally benign alternatives to classical solvents. Among the various desirable characteristics of ESs, pH is a key property with significant implications for the design and control of industrial-scale applications. However, selecting an ES with the required pH for a particular application is a challenging task, especially with extensive experimentally determined data being time consuming and expensive. Therefore, in this work, the pH of various ESs have been predicted via novel quantitative structure−property relationships (QSPR) models using two machine learning algorithms, a multiple linear regression (MLR) and an artificial neural network (ANN), with a set of molecular descriptors generated by COSMO-RS. A total of 648 experimental points for 41 chemically unique ESs prepared from 9 HBAs and 21 HBDs at different temperatures were utilized for sufficient data set representation. On the basis of the statistical analysis of the models, it can be concluded that both approaches can be utilized as powerful predictive tools in estimating the pH of new ESs with the ANN model having better predictive capabilities and the MLR model being more interpretable. These models inspire and stimulate the development of robust models to predict the properties of designer solvents from the drawn molecular structures, which will save time and resources.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Lemaoui

Hatab

Darwish

et al. 2021

ACS Sustainable Chem. Eng.

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“…Analyzing the drug-likeness and physicochemical profile of novel bioactive compounds is key to drug discovery and development [ 45 , 46 ]. Lipinski's rule is the gold standard accepted as a filter for assessing drug-like properties of compounds based on these four (4) parameters and their threshold values: molecular weight (MW) < 500, lipophilicity (log P) < 5, number of hydrogen bond acceptors (HBA) < 10, and hydrogen bond donors (HBD) < 5 [ 47 ]. The drug-like nature of a compound is certified only if it does not violate more than three (3) of the parameters proposed by Lipinski [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Chemo-informatics applications in the design of novel 7-keto-sempervirol derivatives as SmCB1 inhibitors with potential for treatment of Schistosomiasis

Anyubaga,

Shallangwa,

Uzairu

et al. 2024

Heliyon

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“…The prediction accuracy of the constructed MLP was evaluated using multiple statistical criteria, considering the coefficient of determination ( R 2 ), the root-mean-square error ( RMSE ), the average absolute relative deviation ( AARD ), the mean absolute residual ( MAR ), and the standard deviation ( ASD ), which are expressed as follows: where y exp , y pred , and y̅ therefore provide experimental, predicted, and mean values of CO 2 solubility, respectively, while k denotes the overall data points’ number.…”

Section: Methodsmentioning

confidence: 99%

Predicting the CO₂ Capture Capability of Deep Eutectic Solvents and Screening over 1000 of their Combinations Using Machine Learning

Lemaoui,

Boublia,

Lemaoui

et al. 2023

ACS Sustainable Chem. Eng.

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Deep eutectic solvents (DESs) are a new class of environmentally friendly solvents that have attracted the attention of many researchers. Since DESs have several practical applications in CO 2 capture, knowledge of their CO 2 solubility is crucial. In this study, the CO 2 solubility of DESs was predicted via a multilayer perceptron (MLP) using molecular descriptors derived from the Conductor-like Screening Model for Real Solvents (COSMO-RS). An extensive database of 2327 data points was created from 94 unique DES mixtures made from 2 anions, 17 cations, and 39 hydrogen bond donors (HBDs) at 150 different compositions and operating conditions of temperatures and pressures. Several statistical tests were performed, and after thorough hyperparameter tuning, it was found that the best MLP architecture predicted the CO 2 solubility with an R 2 value of 0.986 ± 0.002 and an average absolute relative deviation (AARD) of 4.504 ± 0.507. The MLP has also been loaded into an accessible Excel spreadsheet included in the Supporting Information. Thereafter, in order to guide DES molecular design for achieving high CO 2 solubilities, the MLP was utilized for the high-throughput screening of 1320 DES combinations. This model encourages the creation of robust and accurate models to predict the CO 2 solubility of novel DESs, which will minimize the need for conducting costly and time-consuming experiments.

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In silico drug discovery of IKK-β inhibitors from 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives based on QSAR, docking, molecular dynamics and drug-likeness evaluation studies

Cited by 22 publications

References 60 publications

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Chemo-informatics applications in the design of novel 7-keto-sempervirol derivatives as SmCB1 inhibitors with potential for treatment of Schistosomiasis

Predicting the CO₂ Capture Capability of Deep Eutectic Solvents and Screening over 1000 of their Combinations Using Machine Learning

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In silico drug discovery of IKK-β inhibitors from 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives based on QSAR, docking, molecular dynamics and drug-likeness evaluation studies

Cited by 22 publications

References 60 publications

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Chemo-informatics applications in the design of novel 7-keto-sempervirol derivatives as SmCB1 inhibitors with potential for treatment of Schistosomiasis

Predicting the CO2 Capture Capability of Deep Eutectic Solvents and Screening over 1000 of their Combinations Using Machine Learning

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Predicting the CO₂ Capture Capability of Deep Eutectic Solvents and Screening over 1000 of their Combinations Using Machine Learning