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.

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.

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 chemometric approach for prediction of antifungal activity of some benzoxazole derivatives against Candida albicans

2012 ◽  
pp. 273-282 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Lidija Jevric ◽  
Strahinja Kovacevic ◽  
Natasa Kalajdzija
Keyword(s):  
Candida Albicans ◽  
Antifungal Activity ◽  
Clinical Importance ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Parameters ◽  
Relationship Analysis ◽  
Calibration Models ◽  
Chemometric Approach ◽  
Leave One Out

The purpose of the article is to promote and facilitate prediction of antifungal activity of the investigated series of benzoxazoles against Candida albicans. The clinical importance of this investigation is to simplify design of new antifungal agents against the fungi which can cause serious illnesses in humans. Quantitative structure activity relationship analysis was applied on nineteen benzoxazole derivatives. A multiple linear regression (MLR) procedure was used to model the relationships between the molecular descriptors and the antifungal activity of benzoxazole derivatives. Two mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds against Candida albicans. The quality of the models was validated by the leave-one-out technique, as well as by the calculation of statistical parameters for the established model.

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.

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.

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.

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.

Prediction of Soil Sorption (Koc) of Herbicides Using Semiempirical Molecular Properties

Weed Science ◽  
1994 ◽  
Vol 42 (3) ◽  
pp. 453-461 ◽  
Author(s):  
Krishna N. Reddy ◽  
Martin A. Locke
Keyword(s):  
Molecular Orbital ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Properties ◽  
Soil Sorption ◽  
Van Der Waals Volume ◽  
Highest Occupied Molecular Orbital ◽  
Mechanical Methods ◽  
Modeling Software ◽  
Qsar Models ◽  
Lowest Unoccupied Molecular Orbital

Relationships between soil sorption normalized to organic carbon (Koc) and molecular properties of 71 herbicides were examined. The Koc values were obtained from the literature. Various molecular properties were calculated by quantum mechanical methods using molecular modeling software. The quantitative structure activity relationship (QSAR) models based on four molecular properties, van der Waals volume (VDWv), molecular polarizability (α), dipole moment (μ), and energy of highest occupied molecular orbital (eHOMO), together accounted for 70% of the variation in Koc. Herbicides were broadly divided into six families based on structural similarities, and separate equations were established for each group. The three descriptors, VDWv, α, and μ, along with either energy of lowest unoccupied molecular orbital (eLUMO), or electrophilic superdelocalizability (SE), or eHOMO appeared to be determinants and accounted for 82 to 99% of the variation in Koc. Applicability of these models was tested for one herbicide analogue and 10 metabolites. The QSAR models appear to be specific to structurally similar chemicals. The QSAR models could be developed to predict Koc of structurally similar compounds even before they are synthesized or for some of the metabolites of existing herbicides. Models of this type can also be developed to create priority lists for testing, so that time, money, and efforts can be focused on the potentially most hazardous chemicals.

STRUCTURAL FEATURE STUDY OF QUINOLINES DERIVATIVES WITH THERMODYNAMIC AND OTHER DESCRIPTORS: A QSAR APPROACH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (09) ◽  
pp. 21-26
Author(s):  
Mukesh C. Sharma ◽  
Dharm V. Kohli ◽  
Keyword(s):  
Antihypertensive Agents ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Stepwise Multiple Regression ◽  
Angiotensin Ii Receptor ◽  
Method Of Least Squares ◽  
Chemical Thermodynamic ◽  
Relationship Analysis ◽  
Structure Activity ◽  
Qsar Models

Quantitative structure activity relationship analysis was performed on a series of thirty-three quinoline derivatives to establish the structural features required for angiotensin II receptor activity. QSAR models were derived by stepwise multiple regression analysis employing the method of least squares, using quantum chemical, thermodynamic, electronic and steric descriptors. Model showed best predictability of activity with cross validated value (q2 ) =0.7485, coeffi cient of determination (r2 ) =0.8734 and standard error of estimate (s) = 0.2690. These guidelines may be used to develop new antihypertensive agents based on the quinoline analogues scaffold.

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.

Sign in / Sign up

Export Citation Format

Share Document