Novel and predictive QSAR model for steroidal and nonsteroidal 5α-Reductase type II Inhibitors

2020 ◽  
Vol 17 ◽  
Author(s):  
Huda Mando ◽  
Ahmad Hassan ◽  
Sajjad Gharaghani
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Energy Relation ◽  
Type Ii ◽  
Active Inhibitor ◽  
Linear Free Energy ◽  
Benign Prostate Hypertrophy ◽  
External Data

: In this study, novel quantitative structure activity relationship (QSAR) model has been developed for inhibitors of human 5-alpha reductase type II, which are used to treat benign prostate hypertrophy (BPH). The dataset consisted of 113 compounds-mainly nonsteroidal-with known inhibitory concentration. Then 3D structures of compounds were optimized and molecular structure descriptors were calculated. The stepwise multiple linear regression was used to select descriptors encoding the inhibitory activity of the compounds. Multiple linear regression (MLR) was used to build up the linear QSAR model. The results obtained revealed that the descriptors which best describe the activity were atom type electropological state, carbon type, radial distribution function (RDF), barysz matrix and molecular linear free energy relation. The suggested model could achieve satisfied square correlation coefficient of R2 = 0.72, higher than of many previous studies, indicating its superiority. Rigid validation criteria were met using external data with Q2 ˃ 0.5 and R2 = 0.75 reflecting the predictive power of the model. The QSAR model was applied for screening botanical components of herbal preparations used to treat BPH, and could predict the activity of some among others, making reasonable attribution to the proposed effect of these preparations. Gamma tocopherol, was found to be active inhibitor, in consistence with many previous studies, anticipating the power of this model in prediction of new candidate molecules and suggesting further investigations.

scholarly journals QSAR Study of Indolylisoxazoline Analogues for Their Antiprostat Activity

2019 ◽  
Vol 1 (2) ◽  
pp. 66-71
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molecular Mechanics ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Relationship Study

ABSTRACT Quantitative Structure-Activity relationship study of 13 Indolylisoxazoline analogues as antiprostat agent using multiple linear regression (MLR) has been done. Geometri optimization of Indolylisoxazoline analogues using molecular mechanics (MM+) method. The best QSAR model from regression analysis is log IC50 Pred = 30,877 - 71,847 x qC10 + 165,070 x qC11 + 70,106 x qC13 with most influents descriptor to antiprostat activity are qC10, qC11 dan qC13.

QSAR and Structure-Based Docking Studies of Aryl Pyrido[2,3-d]pyrimidin-7(8H)-ones

2018 ◽  
Vol 3 (1) ◽  
pp. 43-73 ◽  
Author(s):  
Jyoti Durgapal ◽  
Neha Bisht ◽  
Muneer Alam ◽  
Dipiksha Sharma ◽  
Mohd Salman ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Model Validation ◽  
Biological Activities ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Docking Studies ◽  
Linear Regression Method ◽  
Qsar Models

The target of the present study has been to carry out computer-aided anticancer drug design utilizing genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure activity relationship (QSAR) of fibroblast growth factor (FGFr) inhibition of pyrido[2,3-d]pyrimidine-7(8H)-one compounds utilizing different classes of computed structural descriptors. A QSAR model was developed utilizing a combination of constitutional, functional group, geometrical and atom-centered fragment indices by multiple linear regression method and the model validation was performed by searching the predictability of the QSAR models. After outlier analyses through applicability domain, the model validation results were improved. In this connection, molecular docking studies were performed to predict the mode of binding and important structural features necessary for producing biological activities. This attempt could be helpful for further modeling of potent less toxic anticancer chemotherapeutics in these congeners.

scholarly journals Application of multiple linear regression analysis to predict antifungal activity of some benzimidazole derivatives using ADME parameters

2013 ◽  
pp. 239-247
Author(s):  
Natasa Kalajdzija ◽  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic ◽  
Lidija Jevric ◽  
Strahinja Kovacevic
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Antifungal Activity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Qsar Model ◽  
Multiple Linear Regression Analysis ◽  
Quantitative Structure Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Statistical Parameters

In this study we were investigated the relationship between the antifungal activity of some benzimidazole derivatives and some absorption, distribution, metabolism and excretion (ADME) parameters. The antifungal activity of studied compounds against Saccharomyces cerevisiae was expressed as the minimal inhibitory concentration (MIC). A statistically significant quantitative structure-activity relationship (QSAR) model for predicting antifungal activity of the investigated benzimidazole derivatives against Saccharomyces cerevisiae was obtained by multiple linear regression (MLR) using ADME parameters. The quality of the MLR model was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed model, and the results are discussed based on the statistical data.

scholarly journals Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors

2019 ◽  
pp. 1-15 ◽  
Author(s):  
Leila Emami ◽  
Razieh Sabet ◽  
Amirhossein Sakhteman ◽  
Mehdi Khoshnevis Zade
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Quantitative Structure Activity Relationship ◽  
Dipeptidyl Peptidase ◽  
Docking Studies ◽  
Quantitative Structure ◽  
Dipeptidyl Peptidase 4 ◽  
Structure Activity

Type 2 diabetes (T2DM) is a metabolic disorder disease and DPP-4 inhibitors are a class of oral hypoglycemic that blocks the dipeptidyl peptidase-4 (DPP-4) enzyme.  DPP-4 inhibitors reduce glucagon and blood glucose levels and don’t have side effects such as hypoglycemia or weight gain. In this paper, a series of imidazolopyrimidine amides analogues as DPP4 inhibitors were selected for quantitative structure-activity relationship (QSAR) analysis and docking studies. A collection of chemometric methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), genetic algorithm for variable selection-MLR (GA-MLR) and partial least squared combined with genetic algorithm for variable selection (GA-PLS), were conducted to make relations between structural features and DPP4 inhibitory of a variety of imidazolopyrimidine amides derivatives. GA-PLS represented superior results with high statistical quality (R2 = 0.94 and Q2 = 0.80) for predicting the activity of the compounds. Docking studies of these compounds reveals and confirms that compounds 15, 18, 25, 26, and 28 are introduced as good candidates for DPP-4 inhibitors were introduced as a good candidate for DPP-4 inhibitory compounds.

QSAR Models for Predicting Aquatic Toxicity of Esters Using Genetic Algorithm-Multiple Linear Regression Methods

2019 ◽  
Vol 22 (5) ◽  
pp. 317-325
Author(s):  
Mehdi Rajabi ◽  
Fatemeh Shafiei
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Biological Activities ◽  
Aquatic Toxicity ◽  
Qsar Model ◽  
Data Set ◽  
Test Set ◽  
Aliphatic Esters

Aim and Objective: Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure–Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors. Materials and Methods: A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities. Results: The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263. Conclusion: The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.

scholarly journals QSAR study on the histamine (H3) receptor antagonists using the genetic algorithm: Multi parameter linear regression

2012 ◽  
Vol 77 (5) ◽  
pp. 639-650 ◽  
Author(s):  
Maryam Adimi ◽  
Mahmoud Salimi ◽  
Mehdi Nekoei
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Qsar Study ◽  
Data Set ◽  
Histamine H3 Receptors ◽  
H3 Receptors ◽  
Histamine H3

A quantitative structure activity relationship (QSAR) model has been produced for predicting antagonist potency of biphenyl derivatives as human histamine (H3) receptors. The molecular structures of the compounds are numerically represented by various kinds of molecular descriptors. The whole data set was divided into training and test sets. Genetic algorithm based multiple linear regression is used to select most statistically effective descriptors. The final QSAR model (N =24, R2=0.916, F = 51.771, Q2 LOO = 0.872, Q2 LGO = 0.847, Q2 BOOT = 0.857) was fully validated employing leaveone- out (LOO) cross-validation approach, Fischer statistics (F), Yrandomisation test, and predictions based on the test data set. The test set presented an external prediction power of R2 test=0.855. In conclusion, the QSAR model generated can be used as a valuable tool for designing similar groups of new antagonists of histamine (H3) receptors.

scholarly journals Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using Quantitative Structure Activity Relationship Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Samir Chtita ◽  
Mounir Ghamali ◽  
Rachid Hmamouchi ◽  
Bouhya Elidrissi ◽  
Mohamed Bourass ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Artificial Neural Network Ann ◽  
Antileishmanial Activities ◽  
New Compounds

In a search of newer and potent antileishmanial (against promastigotes and amastigotes form of parasites) drug, a series of 60 variously substituted acridines derivatives were subjected to a quantitative structure activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds by using multiple linear regression and artificial neural network (ANN) methods. The used descriptors were computed with Gaussian 03, ACD/ChemSketch, Marvin Sketch, and ChemOffice programs. The QSAR models developed were validated according to the principles set up by the Organisation for Economic Co-operation and Development (OECD). The principal component analysis (PCA) has been used to select descriptors that show a high correlation with activities. The univariate partitioning (UP) method was used to divide the dataset into training and test sets. The multiple linear regression (MLR) method showed a correlation coefficient of 0.850 and 0.814 for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. Internal and external validations were used to determine the statistical quality of QSAR of the two MLR models. The artificial neural network (ANN) method, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.933 and 0.918 with 7-3-1 and 6-3-1 ANN models architecture for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outsides compounds. The effects of different descriptors in the activities were described and used to study and design new compounds with higher activities compared to the existing ones.

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