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.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4795
Author(s):  
Ajaykumar Gandhi ◽  
Vijay Masand ◽  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Anis Ben Ghorbal ◽  
...  
Keyword(s):  
De Novo ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Structure Activity ◽  
Tnbc Cell ◽  
Qsar Models ◽  
Anti Tumor Activity

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

scholarly journals Machine Learning for the Design and Development of Biofilm Regulators

2018 ◽  
Author(s):  
Benjamin Stone ◽  
Erik Sapper
Keyword(s):  
Machine Learning ◽  
Quorum Sensing ◽  
Computational Models ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Machine Learning Algorithms ◽  
Chemical Signaling ◽  
Biochemical System ◽  
Structure Activity ◽  
Qsar Models

Biofilms are congregations of bacteria on a surface, and they grow into obstacles for the functionalities of any device or machinery involves anything biological. Biofilms are developed through a biochemical system known as &lsquo;Quorum Sensing&rsquo; that accounts for the chemical signaling that direct either biofilm formation or inhibition. Computational models that relate chemical and structural features of compounds to their performance properties have been used to aide in the discovery of active small molecules for many decades. These quantitative structure-activity relationship (QSAR) models are also important for predicting the activity of molecules that can have a range of effectiveness in biological systems. This study uses QSAR methodologies combined with and different machine learning algorithms to predict and assess the performance of several different compounds acting in Quorum Sensing. Through computational probing of the quorum sensing molecular interaction, new design rules can be elucidated for countering biofilms.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

QUANTITATIVE STRUCTURE - ACTIVITY RELATIONSHIP STUDIES OF SOME BENZIMIDAZOLE ANALOGUES BASING ON PARTIAL LEAST SQUARES METHOD

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (11) ◽  
pp. 15-21
Author(s):  
M. C Sharma ◽  
◽  
D. V. Kohli
Keyword(s):  
Least Squares Method ◽  
Antihypertensive Agents ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Quantitative Structure ◽  
Angiotensin Ii Receptor ◽  
Data Set ◽  
Structure Activity

Quantitative structure-activity relationship studies on benzimidazole derivatives as angiotensin II receptor were performed to explore the structural requirements for biologic activity. QSAR model obtained was statistically significant with r2 value of 0.8847, cross validated correlation coefficient q2 value of 0.7538 and pred_r2 value of 0. 8135, coefficient of correlation of predicted data set (pred_r2se) 0.5487 was developed by stepwise PLSR method. The observed activity of these molecules was consistent with the model, suggesting that the model may be useful in the design of potent antihypertensive agents.

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