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.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

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 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 QSAR Study of N-Myristoyltransferase Inhibitors of Antimalarial Agents

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2348 ◽  
Author(s):  
Letícia Santos-Garcia ◽  
Marco de Mecenas Filho ◽  
Kamil Musilek ◽  
Kamil Kuca ◽  
Teodorico Ramalho ◽  
...  
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
New Drugs ◽  
Protozoan Parasites ◽  
Training Set ◽  
First Line ◽  
Qsar Study ◽  
Antimalarial Agents ◽  
Genus Plasmodium ◽  
Qsar Models ◽  
Test Sets

Malaria is a disease caused by protozoan parasites of the genus Plasmodium that affects millions of people worldwide. In recent years there have been parasite resistances to several drugs, including the first-line antimalarial treatment. With the aim of proposing new drugs candidates for the treatment of disease, Quantitative Structure–Activity Relationship (QSAR) methodology was applied to 83 N-myristoyltransferase inhibitors, synthesized by Leatherbarrow et al. The QSAR models were developed using 63 compounds, the training set, and externally validated using 20 compounds, the test set. Ten different alignments for the two test sets were tested and the models were generated by the technique that combines genetic algorithms and partial least squares. The best model shows r2 = 0.757, q2adjusted = 0.634, R2pred = 0.746, R2m = 0.716, ∆R2m = 0.133, R2p = 0.609, and R2r = 0.110. This work suggested a good correlation with the experimental results and allows the design of new potent N-myristoyltransferase inhibitors.

Application of Topological Descriptors in QSAR Modeling: Substituted Hydrazones Used As a Model System

2020 ◽  
Vol 17 (3) ◽  
pp. 253-263
Author(s):  
Vesna Dimova ◽  
Mirjana Stojan Jankulovska
Keyword(s):  
Antimicrobial Activity ◽  
Bacillus Subtilis ◽  
Inhibitory Activity ◽  
Candida Utilis ◽  
Topological Descriptors ◽  
Projection Area ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Qsar Models

Background: QSAR study of p-substituted aromatic hydrazones was performed to estimate the quantitative effects of selected topological descriptors on their antimicrobial activity. None of the hydrazones inhibited the growth of the Aspergillus spp., while the data obtained with regard to the antifungal activity of the compounds against Candida utilis were insufficient to develop reliable statistical QSAR models. Therefore, the investigation was focused on developing QSAR models for predicting the antibacterial activity of the compounds against Bacillus subtilis. Methods: A set of substituted hydrazones were tested for their in vitro growth inhibitory activity against Candida utilis, Bacillus subtilis and Aspergillus niger and the diameter of the inhibition zone (mm) was measured. The inhibitory activity data, determined in μg/mL, were transformed to the negative logarithms of molar MICs (log1/CMIC). Using Marvinsketch software package, 28 topological descriptors were calculated. Statistical parameters, such as R2, Sd, F-test, R2 adj, Q, SPRESS, PSE and Q2, were used to test the quality of the developed two-, three-, four-parametric and higher QSAR models. Results and Conclusion: Statistical evaluation of the data used to test the quality of the obtained QSAR models indicated that the two-parametric model involving the descriptors Atom Count (AC) and Maximal Projection Area (MAPA) was statistically significant when all the statistical parameters were summarized. The two parameters, AC and MAPA, had opposite input in modeling the antimicrobial activity of the selected hydrazones against Bacillus subtilis.

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).

Structural finding of R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans as selective pancreatic β-cells KATP-pβ channel openers

2007 ◽  
Vol 85 (12) ◽  
pp. 1053-1063 ◽  
Author(s):  
Sk. Mahasin Alam ◽  
Soma Samanta ◽  
Amit Kumar Halder ◽  
Soumya Basu ◽  
Tarun Jha
Keyword(s):  
Insulin Secretion ◽  
Negative Impact ◽  
Inhibitory Effect ◽  
Van Der Waals Interactions ◽  
Β Cells ◽  
Pancreatic Β Cells ◽  
Qsar Study ◽  
Water Partition Coefficient ◽  
Qsar Models ◽  
Leave One Out

R/S-3,4-Dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonyl-amino)-2H-1-benzopyrans are pancreatic β-cells potassium (KATP-pβ) channel openers with inhibitory effect on insulin secretion. To find the more active and effective benzopyrans as selective potassium (KATP-pβ) channel openers towards the pancreatic tissues, quantitative structure–activity relationships (QSAR) study was performed using E-state and R-state indices along with Wang–Ford charges, n-octanol/water partition coefficient, molar refractivity, and indicator parameters. QSAR models were developed by statistical techniques, e.g., multiple linear regression (MLR), principle component regression analysis (PCRA), and partial least squares (PLS) analysis. The generated equations were validated by the leave-one-out cross-validation method. The models show the importance of ETSA indices of atom numbers 16, 17, 18, 19, 21 as well as 22. The positive coefficient of S16, S17, S18, S19, S21, and S22 indicate that with the increase of the value of E-state indices, desired activity decreases. RTSA index is also important for the biological activity, and the atom numbers 16, 17, 18, 19, 20 and 22 are involved in van der Waals interactions. RTSA index also possesses negative impact on the inhibition of residual insulin secretion. Wang–Ford charges of some particular atoms are also important for the inhibition. Increase of n-octanol/water partition coefficients of compounds inhibit insulin secretion, and the presence of chlorine atom at m- and p- positions of the phenyl ring B is necessary for the inhibition of residual insulin secretion.Key words: benzopyran derivatives, potassium channel openers, PCRA, PLS, QSAR.

scholarly journals QSAR study of the octanol/water partition coefficient of organophosphorous compounds: The hybrid GA/MLR and GA/ANN approaches

2020 ◽  
Vol 85 (4) ◽  
pp. 467-480 ◽  
Author(s):  
Rana Amiri ◽  
Djelloul Messadi ◽  
Amel Bouakkadia
Keyword(s):  
Partition Coefficient ◽  
External Validation ◽  
Structure Property ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Relationship Analysis ◽  
Water Partition Coefficient ◽  
Variable Subset Selection

This study aimed at predicting the n-octanol/water partition coefficient (Kow) of 43 organophosphorous insecticides. Quantitative structure?property relationship analysis was performed on the series of 43 insecticides using two different methods, linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN), which Kow values of these chemicals to their structural descriptors. First, the data set was separated with a duplex algorithm into a training set (28 chemicals) and a test set (15 chemicals) for statistical external validation. A model with four descriptors was developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm (GA)?variable subset selection (VSS) procedure. The values of statistical parameters, R2, Q2 ext, SDEPext and SDEC for the MLR (94.09 %, 92.43 %, 0.533 and 0.471, respectively) and ANN model (97.24 %, 92.17 %, 0.466 and 0.332, respectively) obtained for the three approaches are very similar, which confirmed that the employed four parameters model is stable, robust and significant.

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.

Evaluation of contribution of different molecular fragments on antibacterial activity of Schiff bases of indole-3-aldehyde based on QSAR study

2013 ◽  
Vol 91 (12) ◽  
pp. 1174-1178
Author(s):  
Priyanka Kamaria ◽  
Neha Kawathekar
Keyword(s):  
Antibacterial Activity ◽  
Schiff Bases ◽  
Positive Contribution ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Modeling Software ◽  
Qsar Models ◽  
Leave One Out ◽  
3D Package ◽  
Dipole Energy

The paper describes the QSAR analysis of a series of 22 Schiff bases of indole-3-aldehyde employing the Hansch approach. Various physicochemical and steric parameters were calculated using the Chem 3D package of molecular modeling Software Chemoffice 2004. QSAR models were generated employing the sequential multiple regression method. Models were validated using leave-one-out and bootstrapping methods. Results obtained show that dipole–dipole energy, LUMO, and total energy play an important role, as their positive contribution is seen in the models. Findings of the present study reveal that substituents that cause increase in flexibility, a decrease in polarity, and electron withdrawing in nature are favorable for antibacterial activity of Schiff bases.

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