scholarly journals Computer-aided molecular design of 2-anilino 4-amino substituted quinazolines derivatives as malarial inhibitors

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Zakari Ya’u Ibrahim ◽  
Adamu Uzairu ◽  
Gideon Adamu Shallangwa ◽  
Stephen Eyije Abechi ◽  
Sulaiman Isyaku
Keyword(s):  
Molecular Design ◽  
Antimalarial Activity ◽  
Quantitative Structure Activity Relationship ◽  
Ionization Potentials ◽  
Statistical Parameters ◽  
Structural Modifications ◽  
Structure Activity ◽  
Material Studio ◽  
The Mean ◽  
Derivatives Of

AbstractQuantitative structure–activity relationship studies conducted on forty-five (45) derivatives of 2-anilino 4-amino substituted quinazolines as malaria inhibitors to determine the structures responsible for their antimalarial properties and design novel derivatives with improved activities. The molecular descriptors generated were selected to develop the theoretical model using the genetic approximation component of the material studio. The developed model found to be a function of ATSC8c, GATS8i, SpMin1_Bhi, JGI10, and TDB6u descriptors, shows excellent statistical parameters (R2 = 0.7913, R2adj = 0.7553, Q2cv = 0.7112, LOF = 0.2125, and R2pred = 0.7650). The mean effect (MF) analysis revealed the descriptor SpMin1_Bhi, as the most influential by its largest percentage contribution (54%) to the developed model. The descriptor decodes the information on the first ionization potentials and was found to have positive MF. Hence, activity increases with increases the descriptor value. Structural modifications of the template (compound 13; pEC50 = 7.387) using electron-withdrawing groups increases the descriptor value (first ionization potentials) of the template, which by extension increases the antimalarial activity lead to the design of ten (10) novel theoretical derivatives with improve antimalarial activities. Compound 3, N4-(3-bromo-5-fluorobenzyl)-N2-(4-fluorophenyl)-6,7-dimethoxyquinazoline-2,4-diamine was found to have the highest antimalarial activities among all the designed derivatives (pEC50 = 8.0515).

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.

QSARs on Bactericidal Activity of 3-carboxy-4-quinolones

2008 ◽  
Vol 59 (2) ◽  
pp. 185-194 ◽  
Author(s):  
Laszlo Tarko ◽  
Lucia Pintilie ◽  
Catalina Negut ◽  
Corneliu Oniscu ◽  
Miron Teodor Caproiu
Keyword(s):  
Escherichia Coli ◽  
Bactericidal Activity ◽  
Molecular Shape ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Specific Criterion ◽  
Structure Activity ◽  
Dependent Property ◽  
Chemical Groups ◽  
Derivatives Of

This paper presents results of three QSAR (Quantitative Structure Activity Relationship) studies realized with the PRECLAV computer program. The database we used contains initially 100 derivatives of 3-carboxy-4-quinolone. The dependent property is bactericidal activity against Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa. A specific criterion identifies the outlier molecules in the calibration set. Two molecules are identified as �possible outliers for lead hopping�. After the elimination of outliers, we obtained: N = 77 / 86 / 84, s = 0.2904 / 0.3583 / 0.2993, r2 = 0.8850 / 0.7943 / 0.8645, F = 91.1 / 37.6 / 82.9 and r2CV = 0.8415 / 0.7337 / 0.8415. The bactericidal activity against the three studied bacteria was favored by the presence of saturated C substituted (hetero)cycles, by the presence of certain groups (-F, unconjugated -NH/-NH2) and by a non-balanced molecular shape. The bactericidal activity was disfavored by the presence of certain chemical groups (-NO2, -C6H4, -CO-) and of the triazole cycle. The lipophilic/hydrophilic feature of quinolones has little impact upon bactericidal activity.

scholarly journals In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum

2020 ◽  
Author(s):  
Zakari Ya’u Ibrahim ◽  
Adamu Uzairu ◽  
Gideon Shallangwa ◽  
Stephen Abechi
Keyword(s):  
In Silico ◽  
Density Functional ◽  
Antimalarial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Basis Set ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Leave One Out

Abstract A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

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