Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine

2012 ◽  
Vol 47 (2) ◽  
pp. 421-429 ◽  
Author(s):  
Hassan Golmohammadi ◽  
Zahra Dashtbozorgi ◽  
William E. Acree
Keyword(s):  
Support Vector Machine ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Partitioning Behavior

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.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

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