scholarly journals Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

2015 ◽  
Vol 80 (2) ◽  
pp. 187-196 ◽  
Author(s):  
Zhila Avval ◽  
Eslam Pourbashir ◽  
Mohammad Ganjali ◽  
Parviz Norouzi
Keyword(s):  
Genetic Algorithm ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multiple Linear Regressions ◽  
Stability Robustness ◽  
Comparison Of The Results ◽  
New Compounds ◽  
Linear Regressions ◽  
Mlr Model

This paper deals with developing a linear quantitative structure-activity relationship (QSAR) model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-?] indole, diazepino [1,2-?] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR) technique combined with the stepwise (SW) and the genetic algorithm (GA) methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

scholarly journals QSAR Analysis of Aminoquinoline Analogues as MCH1 Receptor Antagonist

2009 ◽  
Vol 1 (3) ◽  
pp. 594-605 ◽  
Author(s):  
D. K. Patel ◽  
N. M. Patel
Keyword(s):  
Cross Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multiple Linear Regressions ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Melanin Concentrating Hormone ◽  
External Test ◽  
The Relationship ◽  
Linear Regressions

Quantitative structure activity relationship (QSAR) has been established for 2-aminoquinoline-6-carboxamide melanin-concentrating hormone (MCH) 1R antagonists. The multiple linear regressions were used to generate the relationship between biological activity and calculated descriptors. From the 100 of models with r2 > 0.700 was developed. Final selected model was prepared using four descriptors (DMXC, KChiV4, Rcom, and IM1L) which are belong to topological, steric, spatial and electrotopological class descriptor. The validation of the model was done by cross validation; randomization and external test set prediction. The binding pattern of most active compound B15 was postulated based on the pharmacophoric features to further exploration of QSAR model Keywords: QSAR; MCH1 receptor; Obesity; Aminoquinline. © 2009 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.  DOI: 10.3329/jsr.v1i3.2126              J. Sci. Res. 1 (3), 594-605  (2009) 

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

scholarly journals An In Silico Model for Predicting Drug-Induced Hepatotoxicity

2019 ◽  
Vol 20 (8) ◽  
pp. 1897 ◽  
Author(s):  
Shuaibing He ◽  
Tianyuan Ye ◽  
Ruiying Wang ◽  
Chenyang Zhang ◽  
Xuelian Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
New Drugs ◽  
Drug Induced ◽  
Drug Candidates ◽  
External Test

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

scholarly journals Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity

2021 ◽  
Vol 22 (15) ◽  
pp. 8352
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Manoj K. Sabnani ◽  
Abdul Samad
Keyword(s):  
External Validation ◽  
Factor Xa ◽  
Predictive Ability ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Data Set ◽  
Qsar Analysis ◽  
Lead Compounds ◽  
Charged Ring ◽  
Life Threatening

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

QSAR study of IKKβ inhibitors by the genetic algorithm: multiple linear regressions

2013 ◽  
Vol 23 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi
Keyword(s):  
Genetic Algorithm ◽  
Multiple Linear Regressions ◽  
Qsar Study ◽  
Linear Regressions

scholarly journals Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija

2019 ◽  
Vol 68 (11-12) ◽  
pp. 573-582 ◽  
Author(s):  
Naima Melzi ◽  
Hamid Zentou ◽  
Maamar Laidi ◽  
Salah Hanini ◽  
Yamina Ammi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Molecular Diffusion ◽  
Mean Squared Error ◽  
External Validation ◽  
Multiple Linear Regressions ◽  
Mean Squared Errors ◽  
Wide Range ◽  
Artificial Neural ◽  
Linear Regressions

In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext). The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2).

A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (04) ◽  
pp. 22-31
Author(s):  
M. C Sharma ◽  
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Least Square ◽  
Qsar Study ◽  
Partial Least Square Analysis ◽  
Qsar Models

A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.

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