scholarly journals QSAR STUDY OF 1,10-PHENANTHROLINE DERIVATIVES AS THE ANTIMALARIAL COMPOUNDS USING ELECTRONIC DESCRIPTORS BASED ON SEMIEMPIRICAL AM1 CALCULATION

2010 ◽  
Vol 2 (2) ◽  
pp. 91-96
Author(s):  
Mustofa Mustofa ◽  
Iqmal Tahir ◽  
Jumina Jumina
Keyword(s):  
Antimalarial Drug ◽  
Quantitative Structure Activity Relationship ◽  
Antiplasmodial Activity ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Activity Data ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Net Charges ◽  
Semi Empirical

Quantitative Structure-Activity Relationship (QSAR) analysis of 1,10-phenantroline analogs as antimalarial drug has been conducted using atomic net charges (q) as predictors of their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities are taken as the activity of the drugs against plasmodium falciparum (FcM29-Cameroun) strain and are presented as the value of ln(1/IC50) where IC50 is an effective concentration inhibiting 50 % of the parasite growth.  The results show that there is correlation between antiplasmodial activity and electronic structure as represented by a linear function of activity versus atomic net charges of N1, C7, C10, C14 atoms on the 1,10-phenanthroline skeleton and is expressed by : log IC50 = -3,4398 - 14,9050 qN1 - 8,5589 qC10 - 14,7565 qC7 + 5,0457 qC11 The equation is significant at 95% level with statistical parameters : n = 13; r = 0,96275; r2 = 0,92689; SE = 0,61578 and F (4,8) = 25,3556.   Keywords: antimalarial drug; 1,10-phenanthroline; QSAR; antiplasmodial activity.

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 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 Study on Toxic Effects of Polar Narcotic Organic

2013 ◽  
Vol 726-731 ◽  
pp. 171-174
Author(s):  
Zhi Min Cao ◽  
Zhen Zhen Wu ◽  
Zhi Fen Lin
Keyword(s):  
Biological Activity ◽  
Toxic Effect ◽  
Quantitative Structure Activity Relationship ◽  
Biological Interactions ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Activity Data ◽  
Inhibitor Molecule ◽  
Specific Effects ◽  
Structure Activity

Quantitative Structure Activity Relationship (QSAR) can provide greater benefits by its application on a larger scale by collecting diverse measurements of biological activity data. It can help in designing effective inhibitors by considering specific effects of various types of substituents, thus reducing trial experiments. This quantitative technology can be utilized to improve the structure of the inhibitor molecule and to interpret the improved structure in terms of favorable biological interactions. In this paper, toxic effect of polar narcotic organic is analyzed by QSAR study method.

Inhibition of UDP-glucuronosyltransferase by 5'-O-amino Acid and Oligopeptide Derivatives of Uridine: Structure-Activity Relationships

1998 ◽  
Vol 53 (3-4) ◽  
pp. 173-181 ◽  
Author(s):  
Zlatina G. Naydenova ◽  
Konstantin C. Grancharov ◽  
Dimitar K. Alargov ◽  
Evgeny V. Golovinsky ◽  
Ivanka M. Stanoeva ◽  
...  
Keyword(s):  
Amino Acid ◽  
Kinetic Studies ◽  
Quantitative Structure Activity Relationship ◽  
Inhibitory Effect ◽  
Minimum Length ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Udp Glucuronosyltransferase ◽  
Derivatives Of

Abstract The inhibitory effect of a series of 5′-O-amino acid and oligopeptide derivatives of uridine on rat liver UDP-glucuronosyltransferase (UGT) activities was investigated using two assay systems. A quantitative structure-activity relationship (QSAR) study was performed. The compounds include a lipophilic residue linked to the nucleoside by a variable spacer. More­ over, half of the derivatives have two spacers linked to the uridine moiety. Compound 1, a serine derivative of isopropylideneuridine, was found to be the most potent inhibitor of both 4-nitrophenol (4-NP) and phenolphthalein (PPh) glucuronidation, with an I50 of 0.45 mᴍ and 0.22 mᴍ , respectively. Kinetic studies with this substance revealed a mixed type of inhibition towards 4-NP and UDP-glucuronic acid, with apparent Ki values of 150 μᴍ and 120 μᴍ , respectively. The dipeptide derivatives 11-14 exhibited a low activity against 4-NP conjuga­ tion. However, a marked suppression of PPh glucuronidation was found with compounds 11 and 13. Generally, compounds with two spacers are more inhibitory against the UGT activi­ties studied. The QSAR analysis outlined the significance of the spacers with a minimum length of 5 atoms and lipophilic residues linked to them for the inhibitory effect of the compounds. The most significant contribution to this effect is given by the six-atom spacer for both, 4-NP and PPh substrates. 4-NP converting UGT isoforms seem to respond more specifically to the inhibitors: a five-atom for the first and a six-atom for the second spacer enhance binding to both 4-NP and PPh conjugating isoenzymes, while a long second spacer contributes to inhibitor binding to UGT isoforms only converting PPh.

scholarly journals Quantitative Structure–Activity Relationships for Structurally Diverse Chemotypes Having Anti-Trypanosoma cruzi Activity

2019 ◽  
Vol 20 (11) ◽  
pp. 2801 ◽  
Author(s):  
Anacleto S. de Souza ◽  
Leonardo L. G. Ferreira ◽  
Aldo S. de Oliveira ◽  
Adriano D. Andricopulo
Keyword(s):  
Trypanosoma Cruzi ◽  
Predictive Ability ◽  
Physicochemical Characteristics ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Least Squares Regression ◽  
Molecular Scaffolds ◽  
Activity Data ◽  
Qsar Analysis ◽  
Structure Activity

Small-molecule compounds that have promising activity against macromolecular targets from Trypanosoma cruzi occasionally fail when tested in whole-cell phenotypic assays. This outcome can be attributed to many factors, including inadequate physicochemical and pharmacokinetic properties. Unsuitable physicochemical profiles usually result in molecules with a poor ability to cross cell membranes. Quantitative structure-activity relationship (QSAR) analysis is a valuable approach to the investigation of how physicochemical characteristics affect biological activity. In this study, artificial neural networks (ANNs) and kernel-based partial least squares regression (KPLS) were developed using anti-T. cruzi activity data for broadly diverse chemotypes. The models exhibited a good predictive ability for the test set compounds, yielding q2 values of 0.81 and 0.84 for the ANN and KPLS models, respectively. The results of this investigation highlighted privileged molecular scaffolds and the optimum physicochemical space associated with high anti-T. cruzi activity, which provided important guidelines for the design of novel trypanocidal agents having drug-like properties.

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 to elucidate human CYP2A6 inhibition by organosulfur compounds

ADMET & DMPK ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 196-209
Author(s):  
Daniela Andrea Ramirez ◽  
Eduardo Marchevsky ◽  
Juan Maria Luco ◽  
Alejandra Camargo
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
The Body ◽  
Activity Relationship ◽  
Organosulfur Compounds ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Future Design ◽  
Structure Activity

CYP2A6 is a human enzyme responsible for the metabolic elimination of nicotine, and it is also involved in the activation of procarcinogenic nitrosamines, especially those present in tobacco smoke. Several investigations have reported that reducing this enzyme activity may contribute to anti-smoking therapy as well as reducing the risk of promutagens in the body. For these reasons, several authors investigate selective inhibitors molecules toward this enzyme. The aim of this study was to evaluate the interactions between a set of organosulfur compounds and the CYP2A6 enzyme by a quantitative structure-activity relationship (QSAR) analysis. The present work provides a better understanding of the mechanisms involved, with the final goal of providing information for the future design of CYP2A6 inhibitors based on dietary compounds. The reported activity data were modeled by means of multiple regression analysis (MLR) and partial least-squares (PLS) techniques. The results indicate that hydrophobic and steric factors govern the union, while electronic factors are strongly involved in the case of monosulfides.

2D QSAR Analysis of Substituted Quinoxalines for their Antitubercular and Antileptospiral Activities

2019 ◽  
Vol 15 (2) ◽  
pp. 182-192 ◽  
Author(s):  
Ramalakshmi Natarajan ◽  
Ayarivan Puratchikody ◽  
Vignesh Muralidharan ◽  
Mukesh Doble ◽  
Arunkumar Subramani
Keyword(s):  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Good Activity ◽  
Z Score ◽  
Training Set ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Mycobacterium Tuberculosis H37rv ◽  
Structure Activity ◽  
Acceptable Range

Background: The Quantitative structure activity relationship for thirty two novel substituted quinoxalines was performed for their antitubercular (Mycobacterium tuberculosis H37Rv) and antileptospiral (Leptospirainterrogans) activities. The quinoxalines were substituted with azetidinones, thiazolidinones and fluoroquinolones. Several compounds exhibited good activity against both the infections and they all possess fluoroquinolone moiety with the quinoxaline. Methods: The models developed showed good linear relationship (r2 = 0.71-0.88), with an internal predictive ability (q2> 0.61) and good external predictive ability (pred_r2>0.71). The compounds were separated into a training set on which regression was performed and a test set on which the predictive ability of the model was tested. Other statistical parameters including Ro2, Ro’2, k, k’ and Z- score were in the acceptable range. Results and Conclusion: The descriptors obtained explained the necessity of spatial orientation of atoms including branching and adjacency, presence of electronegative groups, balance between lipophilic elements and their binding strengths.

scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR) OF VINCADIFFORMINE ANALOGUES AS THE ANTIPLASMODIAL COMPOUNDS OF THE CHLOROQUINOSENSIBLE STRAIN

2010 ◽  
Vol 5 (3) ◽  
pp. 255-260
Author(s):  
Iqmal Tahir ◽  
Mudasir Mudasir ◽  
Irza Yulistia ◽  
Mustofa Mustofa
Keyword(s):  
Molecular Orbital ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Relationship Analysis ◽  
Structure Activity

Quantitative Structure-Activity Relationship (QSAR) analysis of vincadifformine analogs as an antimalarial drug has been conducted using atomic net charges (q), moment dipole (), LUMO (Lowest Unoccupied Molecular Orbital) and HOMO (Highest Occupied Molecular Orbital) energies, molecular mass (m) as well as surface area (A) as the predictors to their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities were taken as the activity of the drugs against chloroquine-sensitive Plasmodium falciparum (Nigerian Cell) strain and were presented as the value of ln(1/IC50) where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best QSAR model has been determined by multiple linier regression analysis giving QSAR equation: Log (1/IC50) = 9.602.qC1 -17.012.qC2 +6.084.qC3 -19.758.qC5 -6.517.qC6 +2.746.qC7 -6.795.qN +6.59.qC8 -0.190. -0.974.ELUMO +0.515.EHOMO -0.274. +0.029.A -1.673 (n = 16; r = 0.995; SD = 0.099; F = 2.682)   Keywords: QSAR analysis, antimalaria, vincadifformine.  

scholarly journals QUANTITATIVE ELECTRONIC STRUCTURE - ACTIVITY RELATIONSHIPS ANALYSIS ANTIMUTAGENIC BENZALACETONE DERIVATIVES BY PRINCIPAL COMPONENT REGRESSION APPROACH

2010 ◽  
Vol 4 (1) ◽  
pp. 68-75
Author(s):  
Yuliana Yuliana ◽  
Harno Dwi Pranowo ◽  
Jumina Jumina ◽  
Iqmal Tahir
Keyword(s):  
Electronic Structure ◽  
Latent Variables ◽  
Principal Component Regression ◽  
Theoretical Data ◽  
Principal Component ◽  
Antimutagenic Activity ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Net Charges

Quantitative Electronic Structure Activity Relationship (QSAR) analysis of a series of benzalacetones has been investigated based on semi empirical PM3 calculation data using Principal Components Regression (PCR). Investigation has been done based on antimutagen activity from benzalacetone compounds (presented by log 1/IC50) and was studied as linear correlation with latent variables (Tx) resulted from transformation of atomic net charges using Principal Component Analysis (PCA). QSAR equation was determinated based on distribution of selected components and then was analysed with PCR. The result was described by the following QSAR equation : log 1/IC50 = 6.555 + (2.177).T1 + (2.284).T2 + (1.933).T3 The equation was significant on the 95% level with statistical parameters : n = 28 r = 0.766  SE  = 0.245  Fcalculation/Ftable = 3.780 and gave the PRESS result 0.002. It means that there were only a relatively few deviations between the experimental and theoretical data of antimutagenic activity.          New types of benzalacetone derivative compounds were designed  and their theoretical activity were predicted based on the best QSAR equation. It was found that compounds number 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 44, 47, 48, 49 and 50  have  a relatively high antimutagenic activity.   Keywords: QSAR; antimutagenic activity; benzalaceton; atomic net charge

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