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 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 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>

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.

Study on Anti-Tumor Activity of Novel 3-Substituted 4 Anilino-Coumarin Derivatives Using Quantitative Structure-Activity Relationship (QSAR)

2019 ◽  
Vol 948 ◽  
pp. 101-108 ◽  
Author(s):  
Daratu E.K. Putri ◽  
Harno Dwi Pranowo ◽  
Winarto Haryadi
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Linear Regression Method ◽  
Coumarin Derivatives ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Anti Tumor Activity

Study on anti breast cancer activity of 3-substituted 4-anilino coumarin derivatives by using quantitative structure-activity relationship (QSAR) has been performed. The structures and the activity data were literatured from Guoshun et al. experiment. The molecular and electronic molecule properties were obtained from DFT/BPV86 6-31G method calculation after was through methods validation. The QSAR analysis were shown by Multi Linear Regression method (MLR). The best model of obtained for 3-substituted 4-anilino coumarin derivatives is: Log IC50 = 5.905 + (0.936 x qC1) + (-8.225 x qC8) + (-0.582 x qC13) + (11.273 x qC15) + (0.869 x ∆E) ; n = 26; r2= 0.704; Fcal/Ftab = 2.462; SEE = 0.184.

scholarly journals Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 628 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Vibrio Fischeri ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Partial Charge ◽  
Quantitative Structure ◽  
Chemical Structures ◽  
Structure Activity ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.

Understanding the quantitative structure–activity relationship of acetylcholinesterase inhibitors for the treatment of Alzheimer's disease

2015 ◽  
Vol 14 (06) ◽  
pp. 1550040 ◽  
Author(s):  
Anuradha Sharma ◽  
Poonam Piplani
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Structural Basis ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models ◽  
Relationship Of

Alzheimer's disease (AD) is the most common cause of dementia in old aged people and clinically used drugs for treatment are associated with side effects. Thus, there is a current demand for the discovery and development of new potential molecules. However, the recent advances in drug therapy have challenged the predominance of the disease. In this manuscript, an attempt has been made to develop the 2D and 3D quantitative structure–activity relationship (QSAR) models for a series of rutaecarpine, quinazolines and 7,8-dehydrorutaecarpine derivatives to obtain insights to Acetylcholinesterase (AChE) inhibition. Five different QSAR models have been generated and validated using a set of 52 compounds comprising of varying scaffolds with IC50 values ranging from 11,000 nM to 0.6 nM. These AChE-specific prediction models (M1–M5) adequately reflect the structure–activity relationship of the existing AChE inhibitors. Out of all developed models, QSAR model generated using ADME properties has been found to be the best with satisfactory statistical significance (regression (r2) of 0.9309 and regression adjusted coefficient of variation [Formula: see text] of 0.9194). The QSAR models highlight the importance of aromatic moiety as their presence in the structure influence the biological activity. Additional insights on the compounds show that acyclic amines attached to side chain have lower activity than cyclic amines. The QSAR models pinpointing structural basis for the AChEIs suggest new guidelines for the design of novel molecules.

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