scholarly journals Comparison of Two Methods Forecasting Binding Rate of Plasma Protein

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Liu Hongjiu ◽  
Hu Yanrong
Keyword(s):  
Plasma Protein ◽  
Cross Validation ◽  
Nonlinear Models ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Binding Rate ◽  
Structure Activity ◽  
Linear And Nonlinear

By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficientsRcv2of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.

Diaminomaleonitrile derivatives as new potential antichagasic compounds: a study of structure–activity relationships

2021 ◽  
Author(s):  
Aldo S de Oliveira ◽  
Lucas dos S Mello ◽  
Camila H Ogihara ◽  
Thiago H Döring ◽  
David L Palomino-Salcedo ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Schiff Bases ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Binding Energies ◽  
Quantitative Structure ◽  
Hydrophobic Contact ◽  
Structure Activity ◽  
High Yields

Background: Schiff bases are synthetically accessible compounds that have been used in medicinal chemistry. Methods & results: In this work, 27 Schiff bases derived from diaminomaleonitrile were synthesized in high yields (80–98%). Molecular docking studies suggested that the Schiff bases interact with the catalytic site of cruzain. The most active cruzain inhibitor, analog 13 (IC50 = 263 nM), was predicted to form an additional hydrophobic contact with Met68 in the binding site of the enzyme. A strong correlation between the IC50 values and ChemScore binding energies was observed (R = 0.99). Kernel-based 2D quantitative structure–activity relationship models for the whole dataset yielded sound correlation coefficients (R2 = 0.844; Q2 = 0.719). Conclusion: These novel and potent cruzain inhibitors are worthwhile starting points in further Chagas disease drug discovery programs.

scholarly journals Modeling the activity of 2-phenylnaphthalene inhibitors using self-training artificial neural networks

2010 ◽  
Vol 8 (4) ◽  
pp. 877-885 ◽  
Author(s):  
Zahra Garkani-Nejad ◽  
Naser Jalili-Jahani
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Quantitative Structure Activity Relationship ◽  
Multiple Linear Regressions ◽  
Quantitative Structure ◽  
Data Set ◽  
Structure Activity ◽  
Artificial Neural ◽  
Derivatives Of ◽  
Linear Regressions

AbstractThe present study investigates the quantitative structure-activity relationship (QSAR) of 2-phenylnaphthalene ligands on an estrogen receptor (ERα). A data set comprising 70 derivatives of 2-phenylnaphthalene is used. The most suitable parameters, classified as topological, geometric and electronic are selected using a combination of genetic algorithm and multiple linear regression (GA-MLR) methods. Then, selected descriptors are used as inputs for a self-training artificial neural network (STANN). Analysis of the results suggests that the STANN model shows superior results compared to the multiple linear regressions (MLR) by accounting for 91.0% of the variances of the antiseptic potency of the 2-phenylnaphthalene derivatives. The accuracy of the 8-4-1 STANN model is illustrated using leave-multiple-out (LMO) cross-validation and Y-randomization techniques.

STRUCTURAL FEATURE STUDY OF FUROXAN DERIVATIVES AS POTENT ANTI-TRYPANOSOMATID AGENTS: QSAR APPROACH

INDIAN DRUGS ◽  
2016 ◽  
Vol 53 (09) ◽  
pp. 12-21
Author(s):  
M. C. Sharma ◽  
Keyword(s):  
Cross Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Property A ◽  
Qsar Studies ◽  
Structure Activity

Two-dimensional quantitative structure–activity relationship (QSAR) studies of anti-trypanosomatid, furoxan alkylnitrate derivatives have been carried out. This study aims at establishing a quantitative structure activity relationship between furoxan alkylnitrate molecule and their anti-trypanosomatid property. A statistically best QSAR model was obtained with a correlation coefficient r2 of 0.8559, cross validation coefficient, q2 of 0.8072 and pred_r2 value of 0.8217. Various 2D descriptors were calculated and used in the present analysis. The descriptors SdssS (sulfone) count and SdsNE-index suggested that sulphone and NO2 groups at the R1 and R2 positions of furoxan moiety will increases anti-trypanosomatid activity. It will be useful to build a QSAR model to correlate the properties of new untested furoxan derivatives with their anti-trypanosomatid activity.

Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors

2020 ◽  
Vol 16 (5) ◽  
pp. 654-666 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Yao Xi ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Training Set ◽  
Test Set ◽  
Structure Activity ◽  
Consensus Models ◽  
Hiv 1

Background: HIV-1 Integrase (IN) is an important target for the development of the new anti-AIDS drugs. HIV-1 LEDGF/p75 inhibitors, which block the integrase and LEDGF/p75 interaction, have been validated for reduction in HIV-1 viral replicative capacity. Methods: In this work, computational Quantitative Structure-Activity Relationship (QSAR) models were developed for predicting the bioactivity of HIV-1 integrase LEDGF/p75 inhibitors. We collected 190 inhibitors and their bioactivities in this study and divided the inhibitors into nine scaffolds by the method of T-distributed Stochastic Neighbor Embedding (TSNE). These 190 inhibitors were split into a training set and a test set according to the result of a Kohonen’s self-organizing map (SOM) or randomly. Multiple Linear Regression (MLR) models, support vector machine (SVM) models and two consensus models were built based on the training sets by 20 selected CORINA Symphony descriptors. Results: All the models showed a good prediction of pIC50. The correlation coefficients of all the models were more than 0.7 on the test set. For the training set of consensus Model C1, which performed better than other models, the correlation coefficient(r) achieved 0.909 on the training set, and 0.804 on the test set. Conclusion: The selected molecular descriptors show that hydrogen bond acceptor, atom charges and electronegativities (especially π atom) were important in predicting the activity of HIV-1 integrase LEDGF/p75-IN inhibitors.

Quantum chemical and molecular dynamics studies of imidazoline derivatives as corrosion inhibitor and quantitative structure–activity relationship (QSAR) analysis using the support vector machine (SVM) method

2014 ◽  
Vol 13 (02) ◽  
pp. 1450012 ◽  
Author(s):  
Lei Du ◽  
Hongxia Zhao ◽  
Haixiang Hu ◽  
Xiuhui Zhang ◽  
Lin Ji ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Support Vector Machine ◽  
Quantum Chemical ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Hydrophilic Group ◽  
Structure Activity

The inhibition performance of 10 imidazoline molecules with number of carbon from 15 to 21 of hydrocarbon straight-chain was studied by weight-loss method and theoretical approaches. The main purpose was to build a quantitative structure–activity relationship (QSAR) between the structural properties and the inhibition efficiencies, and then to predict efficiencies of new corrosion inhibitors. The quantum chemical calculation suggested that the active region of imidazoline molecules was located on the imidazoline ring and hydrophilic group, and active sites were concentrated on the nitrogen atoms of the molecules and carbon atoms of hydrophilic group. A model in accordance with the real experimental solution was built in the molecular dynamics, and the equilibrium configuration indicated that the imidazoline molecules were adsorbed on Fe (110) surface in parallel manner. Descriptors for QSAR model building were selected by principal component analysis (PCA) and the model was built by the support vector machine (SVM) approach, which shows good performance since the value of correlation coefficient (R) was 0.99 and the root mean square error (RMSE) was 0.94. Additionally, six new imidazoline molecules were theoretically designed and the inhibition efficiencies of three molecules were predicted to be more than 86% by the established QSAR model.

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