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 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 Modeling of Gas-Wall Partitioning of Organic Compounds Using a Quantitative Structure–Activity Relationship

2019 ◽  
Author(s):  
Sanghee Han ◽  
Myoseon Jang ◽  
Huanhuan Jiang
Keyword(s):  
Hydrogen Bond ◽  
Organic Compounds ◽  
Physicochemical Parameters ◽  
Polynomial Equation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
The Impact

Abstract. This study streamlines modeling of the gas–wall process (GWP) of semivolatile organic compounds (SVOC) by predicting gas–wall equilibrium partitioning constant (Kw,i) and accommodation coefficient (αw,i) of SVOC(i) using a quantitative structure–activity relationship. PaDEL-Descriptor, software that calculates molecular descriptors, is employed to obtain physicochemical parameters (i.e., hydrogen bond acidity (Hd,i), hydrogen bond basicity (Ha,i), dipolarity/polarizability (Si), and polarizability (αi)) of SVOC(i). For the prediction of Kw,i, activity coefficients (γw,i) of SVOC(i) to the chamber wall are semiempirically predicted using chamber data in the form of a polynomial equation coupled with the physicochemical parameters. γw,i of various SVOCs differ in functionalities and molecular sizes ranging from 100 to 104. We conclude that the estimation of γw,i is essential to improve the prediction of Kw,i. To predict the impact of relative humidity (RH) on GWP, each coefficient in the polynomial equation for ln(Kw,i) was correlated to RH. Increasing RH enhanced GWP significantly for all polar SVOCs. For example, the predicted Kw,i of 1-heptanoic acid increased more than three times (from 0.58 to 1.96) by increasing RH from 0.4 to 0.75 due to the reduction in γw,i. The characteristic time for GWP are estimated using Kw,i and αw,i to evaluate the effect of GWP on secondary organic aerosol (SOA) mass. It might be significant in the absence of inorganic aerosol, but is insignificant in the presence of electrolytic salts, where aqueous reactions dominate SOA growth.

Predicting the adsorption of organic pollutants on boron nitride nanosheets via in silico techniques: DFT computations and QSAR modeling

2021 ◽  
Author(s):  
Ya Wang ◽  
Weihao Tang ◽  
Yue Peng ◽  
Zhongfang Chen ◽  
Jingwen Chen ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Organic Pollutants ◽  
In Silico ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Boron Nitride Nanosheets ◽  
Structure Activity ◽  
Aqueous Environments ◽  
Qsar Models

Four quantitative structure–activity relationship (QSAR) models were developed for predicting the log K values of organic pollutants adsorbed onto boron nitride nanosheets in gaseous and aqueous environments.

scholarly journals QSAR investigation of acute toxicity of organic compounds during oral administration to mice

2019 ◽  
Vol 65 (2) ◽  
pp. 123-132 ◽  
Author(s):  
O.V. Tinkov ◽  
V.Yu. Grigorev ◽  
P.G. Polishchuk ◽  
A.V. Yarkov ◽  
O.A. Raevsky
Keyword(s):  
Acute Toxicity ◽  
Organic Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Mechanisms Of Action ◽  
Arithmetic Mean ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Qsar Models ◽  
On Line

The effect of the structure of organic compounds on the acute toxicity upon oral injection in mice was studied using 2D simplex representation of the molecular structure and Random forest (RF) methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed (R2 test = 0,61–0,62). The interpretation of the obtained QSAR models was carried out. The contributions of known toxicophores with established mechanisms of action were calculated in order to confirm the ability of the interpretation approach to correctly rank them relative to other structural fragments. The influence of the molecular surroundings of some toxicophores was analyzed. We analyzed the contributions of other highly ranked fragments from the list of common functional groups and ring systems in order to find new potential toxicophores. The on-line version of the expert system “OCHEM” (https://ochem.eu) and Arithmetic Mean Toxicity (AMT) approach were used for a comparative QSAR study.

Sign in / Sign up

Export Citation Format

Share Document