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>

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.

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 Sonochemical Synthesis of 2’-Hydroxy-Chalcone Derivatives with Potential Anti-Oomycete Activity

Antibiotics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 576
Author(s):  
Génesis López ◽  
Marco Mellado ◽  
Enrique Werner ◽  
Bastián Said ◽  
Patricio Godoy ◽  
...  
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Effective Concentration ◽  
Quantitative Structure ◽  
Sonochemical Synthesis ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Half Maximal Effective Concentration ◽  
Amine Groups ◽  
Ultrasound Assisted

This work reports on the synthesis of eight new 2′-hydroxy-chalcones with potential anti-phytopathogenic applications in agroindustry, AMONG others, via Claisen–Schmidt condensation and ultrasound assisted reaction. Assays showed three chalcones with allyl moieties strongly inhibited growth of phytopathogenic oomycete Phytophthora infestans; moreover, compound 8a had a half maximal effective concentration (EC50) value (32.5 µg/mL) similar to that of metalaxyl (28.6 µg/mL). A software-aided quantitative structure–activity relationship (QSAR) analysis of the whole series suggests that the structural features of these new chalcones—namely, the fluoride, hydroxyl, and amine groups over the carbon 3′ of the chalcone skeleton—increase anti-oomycete activity.

QSAR and Ab Initio studies of quinolon-4(1H)-imine derivatives as antimalarial agents

2021 ◽  
Vol 4 (1) ◽  
pp. 192
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo ◽  
Saprini Hamdiani
Keyword(s):  
Antimalarial Activity ◽  
Molecular Orbital Calculation ◽  
Qsar Model ◽  
Basis Set ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

Study on antimalarial activity of 22 quinolon-4(1H)-imine derivatives by using Quantitative Structure-Activity Relationships (QSAR) has been performed. Electronic and molecular descriptors were used in Quantitative Structure-Activity Relationships (QSAR) model and it was obtained from Hartree-Fock (HF) molecular orbital calculation with 6-31G basis set. QSAR analysis has been performed by multiple linear regression (MLR) method. The best equation of QSAR model on this study is: pEC50 = -4,177 + (37,902 x qC3) + (171,282 x qC8) + (9,061 x qC10) + (125,818 x qC11) + (-149,125 x qC17) + (191,623 x qC18), with statistical parameters, n = 22; r2 = 0,910; SEE = 0,171; Fcal/Ftab = 4,510 and PRESS = 0,697. The best equation can applied to design and predict new compounds with higher antimalarial activity.

scholarly journals Design of New Potent Insecticides of Organophosphate Derivatives Based on QSAR Analysis

2013 ◽  
Vol 13 (1) ◽  
pp. 86-93 ◽  
Author(s):  
Mudasir Mudasir ◽  
Yari Mukti Wibowo ◽  
Harno Dwi Pranowo
Keyword(s):  
Sulfonic Acid ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Benzene Sulfonic Acid ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Qsar Models ◽  
New Compounds

Design of new potent insecticide compounds of organophosphate derivatives based on QSAR (Quantitative Structure-Activity Relationship) analytical model has been conducted. Organophosphate derivative compounds and their activities were obtained from the literature. Computational modeling of the structure of organophosphate derivative compounds and calculation of their QSAR descriptors have been done by AM1 (Austin Model 1) method. The best QSAR model was selected from the QSAR models that used only electronic descriptors and from those using both electronic and molecular descriptors. The best QSAR model obtained was:Log LD50 = 50.872 - 66.457 qC1 - 65.735 qC6 + 83.115 qO7 (n = 30, r = 0.876, adjusted r2 = 0.741, Fcal/Ftab = 9.636, PRESS = 2.414 x 10-6)The best QSAR model was then used to design in silico new compounds of insecticide of organophosphate derivatives with better activity as compared to the existing synthesized organophosphate derivatives. So far, the most potent insecticide of organophosphate compound that has been successfully synthesized had log LD50 of -5.20, while the new designed compound based on the best QSAR model, i.e.: 4-(diethoxy phosphoryloxy) benzene sulfonic acid, had log LD50 prediction of -7.29. Therefore, the new designed insecticide compound is suggested to be synthesized and tested for its activity in laboratory for further verification.

scholarly journals Molecular Shape Descriptors. 2. Quantitative Structure-Activity Relationships Based Upon Three-Dimensional Molecular Shape Descriptor

1985 ◽  
Vol 40 (11) ◽  
pp. 1114-1120
Author(s):  
loan Motoc ◽  
Garland R. Marshall
Keyword(s):  
Enzyme Inhibitor ◽  
Three Dimensional ◽  
Shape Descriptor ◽  
Molecular Shape ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
E Coli ◽  
Interactional Pattern ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

A methodology to incorporate the three-dimensional molecular shape descriptor (3 D-MSD) into a quantitative structure-activity relationship is discussed in detail. The 3 D-MSD is calculated and correlated with Kiapp values for a set of 2,4-diamino-5-benzylpyrimidines which inhibit E. coli DHFR. The correlation (n = 22, r = 0.95, s = 0.214, F = 55.10) indicates that the polarization interaction dominates the enzyme-inhibitor interactional pattern.

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