scholarly journals Predicting AAK1/GAK Dual-Target Inhibitor against SARS-CoV-2 Viral Entry into Host Cells: An in silico Approach

2021 ◽  
Vol 1 (1) ◽  
pp. 48-67
Author(s):  
Xavier Chee Wezen ◽  
Clement Sim Jun Wen ◽  
Lilian Siaw Yung Ping ◽  
Yeong Kah Ho ◽  
Kong Hao Qing ◽  
...  
Keyword(s):  
Viral Entry ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Host Cells ◽  
Qsar Models ◽  
Leave One Out ◽  
External Test ◽  
Dual Target

Clathrin-mediated endocytosis (CME) is a normal biological process where cellular contents are transported into the cells. However, this process is often hijacked by different viruses to enter host cells and cause infections. Recently, two proteins that regulate CME – AAK1 and GAK – have been proposed as potential therapeutic targets for designing broad-spectrum antiviral drugs. In this work, we curated two compound datasets containing 83 AAK1 inhibitors and 196 GAK inhibitors each. Subsequently, machine learning methods, namely Random Forest, Elastic Net and Sequential Minimal Optimization, were used to construct Quantitative Structure Activity Relationship (QSAR) models to predict small molecule inhibitors of AAK1 and GAK. To ensure predictivity, these models were evaluated by using Leave-One-Out (LOO) cross validation and with an external test set. In all cases, our QSAR models achieved a q2LOO in range of 0.64 to 0.84 (Root Mean Squared Error; RMSE = 0.41 to 0.52) and a q2ext in range of 0.57 to 0.92 (RMSE = 0.36 to 0.61). Besides, our QSAR models were evaluated by using additional QSAR performance metrics and y-randomization test. Finally, by using a concensus scoring approach, nine chemical compounds from the Drugbank compound library were predicted as AAK1/GAK dual-target inhibitors. The electrostatic potential maps for the nine compounds were generated and compared against two known dual-target inhibitors, sunitinib and baricitinib. Our work provides the rationale to validate these nine compounds experimentally against the protein targets AAK1 and GAK.

scholarly journals Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors

2018 ◽  
Vol 19 (11) ◽  
pp. 3423 ◽  
Author(s):  
Ting Wang ◽  
Lili Tang ◽  
Feng Luan ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Correlation Coefficient ◽  
Binary Mixtures ◽  
Quantitative Structure Activity Relationship ◽  
Training Set ◽  
Statistical Parameters ◽  
Test Set ◽  
Qsar Models ◽  
Forward Stepwise ◽  
Leave One Out ◽  
External Test

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq2 (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R2 = 0.853, qext2 (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R2 = 0.925, LOOq2 = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R2 = 0.896, qext2 = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

scholarly journals Development of QSAR Models Using Singular Value Decomposition Method: A Case Study for Predicting Anti-HIV-1 and Anti-HCV Biological Activities

2021 ◽  
Vol 12 (3) ◽  
pp. 3090-3105
Keyword(s):  
Singular Value Decomposition ◽  
Biological Activities ◽  
Quantitative Structure Activity Relationship ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Qsar Models ◽  
Value Decomposition ◽  
Leave One Out ◽  
External Test ◽  
Hiv 1

This study performed a detailed approach derived by coupling singular value decomposition (SVD) with multiple linear regression (MLR) methods on the performance and predictive capability of the quantitative structure-activity relationship (QSAR). The study was carried out on two different datasets of 128 HIV-1 attachment inhibitors and 115 HCV analogs. For both datasets, the structure of each compound was represented by suitable molecular descriptors. Then, the two datasets were divided into training and test sets employing the Kennard-Stone procedure (K-S). Both MLR and SVD-MLR models were developed to link the structure of the studied compounds to their reported biological activities. The selected models were subjected to the internal leave-one-out cross-validation method, and their predictive abilities were evaluated using the external test set. The developed SVD-MLR models were robust and reliable with an external determination coefficient (R_test^2) of 0.9755 and a mean-square error (MSE) of 0.0205, as well as an R_test^2 of 0.9179 and MSE of 0.0298 for the HCV and the HIV set, respectively. In return, this model could be developed to predict the activities of a non-seen extra set of organic molecules for the purpose of either virtual screening or lead/hit optimization.

scholarly journals QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

2014 ◽  
Vol 79 (9) ◽  
pp. 1111-1125 ◽  
Author(s):  
Dan-Dan Wang ◽  
Lin-Lin Feng ◽  
Guang-Yu He ◽  
Hai-Qun Chen
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Tetrahymena Pyriformis ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Qsar Studies ◽  
Qsar Models ◽  
Leave One Out

Quantitative structure-activity relationship (QSAR) models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS) was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl?Cl), Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR) method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944). Moreover, Y-scrambling test indicated there was no chance correlation in this model.

Chemometric Modeling of the Ecotoxicity of Industrial Chemicals to an Avian Species Anas Platyrhynchos

2020 ◽  
Vol 5 (2) ◽  
pp. 1-16
Author(s):  
Shinjita Ghosh ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Anas Platyrhynchos ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Avian Species ◽  
Industrial Chemicals ◽  
Linear Discriminant ◽  
Topological Distance ◽  
Qsar Models ◽  
Classification And Regression

Birds or avians have been imperative species in the ecology, having been evaluated in an effort to understand the toxic effects of endocrine disruption. The ecotoxicity of 56 industrial chemicals classified as endocrine disruptors were modeled employing classification and regression-based quantitative structure-activity relationship (QSAR) models to an important avian species, Anas platyrhynchos. The classification- and regression-based QSAR models were developed using linear discriminant analysis (LDA) and partial least squares (PLS) tools, respectively. All models were validated meticulously by employing internal and external validation metrics followed by randomization test, applicability domain (AD) study, and intelligent consensus prediction of all individual models. Features like topological distance of 1, 3, and 5 between atoms O-P, C-P, and N-S, correspondingly, along with the CR3X fragment, can be responsible for an increase in toxicity. On the contrary, the presence of S-Cl with topological distance 6 is accountable for lowering the toxicity of towards A. platyrhynchos. The developed chemometric models can offer significant evidence and guidance in the framework of virtual screening as well as a toxicity prediction of new and/or untested chemical libraries towards this specific avian species.

scholarly journals A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wen ◽  
Qing Li ◽  
Wei Li ◽  
Qiao Cai ◽  
Yong-Ming Cai
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Nonlinear Models ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Study ◽  
Stability Robustness ◽  
Qsar Models ◽  
Leave One Out

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

scholarly journals In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

2020 ◽  
Author(s):  
Zakari Ya’u Ibrahim ◽  
Adamu Uzairu ◽  
Gideon Shallangwa ◽  
Stephen Abechi
Keyword(s):  
In Silico ◽  
Density Functional ◽  
Antimalarial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Basis Set ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Leave One Out

Abstract A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

QSAR Models to Predict Effect of Concentration on the Adsorption of Phenolic Compounds onto XAD-4 and ZH-01

2011 ◽  
Vol 356-360 ◽  
pp. 340-344
Author(s):  
Yun Lan Gu ◽  
Zhen Xing Li ◽  
Zheng Hao Fei ◽  
Gen Cheng Zhang
Keyword(s):  
Phenolic Compounds ◽  
Adsorption Capacity ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Basis Set ◽  
Stepwise Multiple Regression ◽  
Background Concentration ◽  
Qsar Models ◽  
Predicted Values ◽  
Leave One Out

It is assumed that the experimental adsorption capacity of phenolic compounds onto resin depends upon the molecular properties as well as background concentration of the aquatic system. The utility of this concept has been demonstrated by incorporating concentration as a parameter in quantitative structure-activity relationship (QSAR). DFT-B3LYP method, with the basis set 6-311G **, was employed to calculate quantum mechanical and physicochemical descriptors of phenolic compounds. The logarithm of the adsorption capacity of phenolic compounds on XAD-4 and ZH-01 investigated from the static experiment along with the descriptors mentioned above were used to establish QSAR models. The variables were reduced using stepwise multiple regression method, and the statistical results indicated that the correlation coefficient in the multiple linear regression (MLR) and cross-validation using leave-one-out(LOO) were 0.966, 0.920, 0.905 and 0.797, respectively. To validate the predictive power of resulting models, external validation was performed with Qext2 values of 0.927 and 0.849, respectively. The developed models suggest that the adsorption mechanism of phenolic compounds onto XAD-4 and ZH-01 is different. Concentration, hydrophobic parameter are dominant factors governing the adsorption capacity of phenolic compounds onto XAD-4, while concentration and energy of the highest occupied molecular orbital are dominant factors controlling that of phenolic compounds on ZH-01. The consistency between experimental and predicted values indicates that the developed models can be used for estimating adsorption capacity of phenolic compounds onto XAD-4 and ZH-01.

QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum

2013 ◽  
Vol 67 (11) ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Chanin Nantasenamat ◽  
Chartchalerm Isarankura-Na-Ayudhya ◽  
Virapong Prachayasittikul
Keyword(s):  
Plasmodium Falciparum ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Sum Of Squares ◽  
Atomic Masses ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Qsar Models ◽  
Leave One Out

AbstractA data set of amidino bis-benzimidazoles, in particular 2′-arylsubstituted-1H,1′H-[2,5′]bisbenzimidazolyl-5-carboximidine derivatives with anti-malarial activity against Plasmodium falciparum was employed in investigating the quantitative structure-activity relationship (QSAR). Quantum chemical and molecular descriptors were obtained from B3LYP/6-31g(d) calculations and Dragon software, respectively. Significant variables, which included total energy (E T), highest occupied molecular orbital (HOMO), Moran autocorrelation-lag3/weighted by atomic masses (MATS3m), Geary autocorrelation-lag8/weighted by atomic masses (GATS8m), and 3D-MoRSEsignal 11/weighted by atomic Sanderson electronegativities (Mor11e), were used in the construction of QSAR models using multiple linear regression (MLR) and artificial neural network (ANN). The results indicated that the predictive models for both the MLR and ANN approaches using leave-one-out cross-validation afforded a good performance in modelling the anti-malarial activity against P. falciparum as observed by correlation coefficients of leave-one-out cross-validation (R LOO-CV) of 0.9760 and 0.9821, respectively, root mean squared error of leave-one-out cross-validation (RMSELOO-CV) of 0.1301 and 0.1102, respectively, and predictivity of leave-one-out cross-validation (Q LOO-CV2) of 0.9526 and 0.9645, respectively. Model validation was performed using an external testing set and the results suggested that the model provided good predictivity for both MLR and ANN models with correlation coefficient of the external set (R Ext) values of 0.9978 and 0.9844, respectively, root mean squared error of the external set (RMSEExt) of 0.0764 and 0.1302 respectively, and predictivity of the external set (Q Ext2) of 0.9956 and 0.9690, respectively. Furthermore, the robustness of the QSAR models is corroborated by a number of statistical parameters, comprising adjusted correlation coefficient (R Adj2), standard deviation (s), predicted residual sum of squares (PRESS), standard error of prediction (SDEP), total sum of squares deviation (SSY), and quality factor (Q). The QSAR models so constructed provide pertinent insights for the future design of anti-malarial agents.

scholarly journals Linear and Nonlinear QSAR Study of N2 and O6 Substituted Guanine Derivatives as Cyclin-Dependent Kinase 2 Inhibitors

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Nasser Goudarzi ◽  
M. Arab Chamjangali ◽  
Payam Kalhor
Keyword(s):  
Randomization Test ◽  
Cyclin Dependent Kinase ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Guanine Derivatives ◽  
Qsar Models ◽  
Test Sets ◽  
The Mean ◽  
Leave One Out

The inhibitory activities (pIC50) of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors have been successfully modeled using calculated molecular descriptors. Two linear (MLR) and nonlinear (ANN) methods were utilized for construction of models to predict the pIC50 activities of those compounds. The QSAR models were validated by cross-validation (leave-one-out) as well as application of the models for prediction of pIC50 of external set compounds. Also, the models were validated by calculation of statistical parameters and Y-randomization test. Two methods provided accurate predictions, although more accurate results were obtained by ANN model. The mean-squared errors (MSEs) for validation and test sets of MLR are 0.065, 0.069 and of ANN are 0.017 and 0.063, respectively.

STRUCTURAL OPTIMIZATION OF 6 SUBSTITUTED 2-AMINOBENZOTHIAZOLE DERIVATIVES AS ANTIFUNGAL AGENTS

INDIAN DRUGS ◽  
2016 ◽  
Vol 53 (10) ◽  
pp. 12-15
Author(s):  
B Jacob ◽  
◽  
V. Chandy ◽  
L. K. Bisht ◽  
H. Babu ◽  
...  
Keyword(s):  
Partition Coefficient ◽  
Quantitative Structure Activity Relationship ◽  
Homo Energy ◽  
Relationship Analysis ◽  
Multiple Regression Method ◽  
Modeling Software ◽  
Qsar Models ◽  
Statistical Program ◽  
Leave One Out ◽  
3D Package

In the present research fifteen analogues of 6 substituted 2-aminobenzothiazole derivatives displaying variable inhibition of Candida albicans were subjected to quantitative structure activity relationship analysis. Various thermodynamic, electronic and steric parameters were calculated using Chem 3D package of molecular modeling software Chemoffice 8.0. QSAR models were generated employing sequential multiple regression method using in-house statistical program VALSTAT. Statistically significant models with R-values(0.984), R2-(0.9699) and Q2 (0.848) were obtained. Models were validated using leave one out and bootstrapping methods. Results obtained shows that partition coefficient, HOMO energy and VDW Energy are contributing to biological activity. Findings of present study reveals that substituents those alters partition coefficient, HOMO energy and VDW Energy of molecule results in increase in antifungal potency.

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