scholarly journals QSAR STUDY OF FLAVONE / FLAVONOL ANALOGUES AS THE ANTIRADICAL COMPOUNDS BASED ON HANSCH ANALYSIS

2010 ◽  
Vol 3 (1) ◽  
pp. 48-54
Author(s):  
Iqmal Tahir ◽  
Karna Wijaya ◽  
Bambang Purwono ◽  
Dinni Widianingsih
Keyword(s):  
Geometry Optimization ◽  
Semiempirical Method ◽  
Quantitative Structure Activity Relationship ◽  
Heat Of Formation ◽  
Electronic Energy ◽  
Log P ◽  
Multilinear Regression ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Hansch Analysis

Quantitative Structure-Activity Relationship (QSAR) analysis of substituted flavone / flavonol compounds has been carried out by applying Hansch Analysis using their physicochemical properties as the predictors. The properties i.e. log P, (log P)2, core-core interaction energy (Eint), volume (V), molecular mass (M), dipole moment (μ), heat of formation (ΔHof), binding energy (Ei), total energy (ET), surface area (L), polarizability (α), molar refractivity (RM), hidration energy (EH), electronic energy (Eel) and isolated atomic energy (Eat,is), were obtained on the basis of geometry optimization using PM3 semiempirical method. The QSAR analysis used antiradical activities (% A) as the dependent variable and has been done by applying multilinear regression technique. The result showed that QSAR equations i.e. % A  =  77.426 - 67.343  [log P] + 3.160 [(log P)2 + 67.884 [α] + 6.63x10-4 [ Eint] - 5.280 [L] + 1.179 [V] + 0.447 [M] - 11.000 [μ]  + 0.093 [Ei]  + 3.433 [EH] - 3.44x10-3 [ET] (n = 16 ; r2 = 0.987 ; SD = 9.205; Fcal/Ftable = 4.797)   Keywords: QSAR, antiradical, flavone, flavonol

scholarly journals STUDI HUBUNGAN KUANTITATIF STRUKTUR AKTIVITAS SENYAWA TURUNAN MEISOINDIGO SEBAGAI INHIBITOR CDK4

2016 ◽  
Vol 1 (2) ◽  
pp. 129
Author(s):  
Muhammad Arba ◽  
Riki Andriansyah ◽  
Messi Leonita
Keyword(s):  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Heat Of Formation ◽  
Electronic Energy ◽  
Cyclin Dependent Kinase ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Multilinear Regression Analysis ◽  
Qsar Models ◽  
Better Than

ABSTRAKTelah dilakukan analisis Hubungan Kuantitatif Struktur-Aktivitas (HKSA) senyawa turunan meisoindigo sebagai inhibitor Cyclin Dependent Kinase-4 (CDK4) menggunakan regresi multi linear untuk pemilihan variabel. Hasil penelitian menyatakan bahwa aktivitas penghambatan CDK4 dari senyawa turunan mesoindigo bergantung pada beberapa parameter, yaitu momen dipol, energi total, energi elektronik, panas pembentukan, dan kelarutan. Akurasi model HKSA yang diusulkan divalidasi baik dengan teknik validasi silang maupun dengan validasi eksternal. Hasil penelitian ini dapat digunakan untuk desain senyawa inhibitor CDK4 yang lebih baik dari turunan meisoindigo. Kata kunci: HKSA, meisoindigo, kanker, CDK4 ABSTRACTCyclin-dependent kinase 4 (CDK4) is an important target in the treatment of cancer. Exploring of compounds that can inhibit the activity of CDK4 is actively performed worldwide. This research was conducted to do Quantitative Structure-Activity Relationship (QSAR) analysis of meisoindigo derivative compounds as inhibitor for CDK4 in order to get QSAR equation, then it was further used to design new inhibitor based meisoindigo which has more potent and selective for CDK4. Data compound is divided into training set to build QSAR models and the test set to validate the model. Calculation was done by MOE2009.10 descriptor and multilinear regression analysis, SPSS19.0. The results showed that the inhibitory activity of mesoindigo derived compounds toward CDK4 was depended on several dipole moment, total energy, electronic energy, heat of formation, and solubility. The accuracy of QSAR models proposed validated by cross validation techniques and with external validation. The results of this study can be used to design a new CDK4 inhibitor compound better than meisoindigo derivative Keywords: QSAR, meisoindigo, cancer, CDK4

scholarly journals QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP ANALYSIS OF 1,2,4-THIADIAZOLINE FUNGICIDES BASED ON MOLECULAR STRUCTURE CALCULATED BY AM1 METHOD

2010 ◽  
Vol 3 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Mudasir Mudasir ◽  
Iqmal Tahir ◽  
Ida Puji Astuti Maryono Putri
Keyword(s):  
Molecular Structure ◽  
Semiempirical Method ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Properties ◽  
Activity Relationship ◽  
Am1 Method ◽  
Log P ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Relationship Analysis

Quantitative structure-Activity relationship (QSAR) analysis of fungicides having 1,2,4-thiadiazoline structure based on theoretical molecular properties have been done. Calculation of the properties was conducted by semiempirical method AM1 and the activity of the compounds was taken from literature. Relationship analysis between fungicides activity (pEC50) and molecular properties was done using SPSS program. The QSAR analysis gave the best model as follows: pEC50 = 3.842 + (1.807x10-4) ET + (5.841x10-3) Eb - (5.689x10-2) DHf  -0.770 log P + 1.144 a - 0.671 m + 9.568 GLOB - (5.54x10-2) MR. n=19   r=0.917   SE=0.216   Fcal/Ftable=2.459   PRESS=0.469. The best model obtained was then used to design and predict the fungicides activity of new compounds derived from 1,2,4-thiadiazoline.   Keywords: QSAR, QSPR, fungicide, molecular structure, 1,2,4-thiadiazoline

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

scholarly journals 2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Prasanna A. Datar
Keyword(s):  
Staphylococcus Aureus ◽  
Pseudomonas Aeruginosa ◽  
Qsar Model ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Comparative Approach ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Leave One Out ◽  
And Staphylococcus Aureus

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

scholarly journals QSAR Analysis of 5-Substituted-2-Benzoyl-aminobenzoic acids as PPAR Modulator

2004 ◽  
Vol 1 (5) ◽  
pp. 243-250 ◽  
Author(s):  
R. Hemalatha ◽  
L. K. Soni ◽  
A. K. Gupta ◽  
S. G. Kaskhedikar
Keyword(s):  
Linear Regression Analysis ◽  
Qsar Model ◽  
Multiple Linear Regression Analysis ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Regression ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Aminobenzoic Acids ◽  
Leave One Out ◽  
Α Activity

A quantitative structure activity relationship (QSAR) study on a series of analogs of 5-aryl thiazolidine-2, 4-diones with activity on PPAR-α and PPAR-γwas made using combination of various thermodynamic, electronic and spatial descriptors. Several statistical regression expressions were obtained using multiple linear regression analysis. The best QSAR model was further validated by leave one out cross validation method. The studied revealed that for dual PPAR-α/γactivity dipole-dipole energy and PMI-Z play significant role and contributed positively for PPAR-γand PPAR-α activity respectively. Thus, QSAR brings important structural insight to aid the design of dual PPAR-α /γreceptor agonist.

scholarly journals A QSAR Modeling on Aurone Derivatives as Antimalarial Agents

2020 ◽  
Vol 32 (11) ◽  
pp. 2839-2845
Author(s):  
R. Hadanau
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multilinear Regression ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Antimalarial Agents ◽  
Inhibition Concentration ◽  
Qsar Models ◽  
Semi Empirical ◽  
Statistical Criteria

A quantitative structure activity relationship (QSAR) analysis was performed on several compound and aurone derivatives (1-16) and 17-21 compounds were used as internal and external tests, respectively. Studies have investigated aurone derivatives; however, for aurone compounds, QSAR analysis has not been conducted. The semi-empirical PM3 method of HyperChem for Windows 8.0 was used to optimise the aurone derivative structures to acquire descriptors. For 15 influential descriptors, the multilinear regression MLR analysis was conducted by employing the backward method, and four new QSAR models were obtained. According to statistical criteria, model 2 was the optimum QSAR model for predicting the inhibition concentration (IC50) theoretical value against novel aurone derivatives. The modelling of 40 (22-61) aurone compounds was achieved. Six novel compounds (54, 55, 58, 59, 60, and 61) were synthesized in a laboratory because the IC50 of these compounds was lower than that of chloroquine (IC50 = 0.14 μM).

scholarly journals QSAR Analysis of Rocaglamide Derivatives Cytotoxic Activities Using LFER Hansch Model

2010 ◽  
Vol 1 (2) ◽  
pp. 129
Author(s):  
Firdayani Firdayani ◽  
Susi Kusumaningrum ◽  
Doddy Irawan Setyo Utomo ◽  
Agung Eru Wibowo ◽  
Chaidir Chaidir
Keyword(s):  
Cancer Cells ◽  
Quantitative Structure Activity Relationship ◽  
Therapeutic Agents ◽  
Log P ◽  
Cytotoxic Activities ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Linear Free Energy ◽  
Naturally Occurring ◽  
Structure Activity

Rocaglamide derivatives are the compounds which have featuring cyclopenta[b]tetra-hydrobenzofuran skeleton. Until now it includes more than 50 naturally occurring derivatives. They were chosen to be interesting candidates for possible therapeutic agents primarily in the field of cancer chemotherapy due to their cytotoxic activities data against various cancer cells. A quantitative structure activity relationship (QSAR) studies were done to investigate physicochemical properties of molecule which contribute to their activities. Series of rocaglamide derivatives have been used and analyzed using linear free energy regression Hansch model for their cytotoxic activities against MONO-MAC-6 leukemia cells, RAJI lymphoma cells and MEL-JUSO melanoma cells. Results showed that the best QSAR equations were revealed involving C Log P and CMR parameters with nonlinear regression relationships in cytotoxic activities of rocaglamide derivatives against cancer cells above. Keywords: QSAR, Rocaglamide, LFER Hansch

MOLECULAR MODELING OF 5‐[(AMIDOBENZYL)OXY]‐ NICOTINAMIDES AS SIRTUIN 2 INHIBITORS USING ALIGNMENT- (IN)DEPENDENT 3D-QSAR ANALYSIS AND MOLECULAR DOCKING

2021 ◽  
Author(s):  
Nemanja Djokovic ◽  
◽  
Ana Postolovic ◽  
Katarina Nikolic
Keyword(s):  
Molecular Docking ◽  
Structural Similarity ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Study Data ◽  
Rational Drug Design ◽  
Qsar Study ◽  
Data Set ◽  
Qsar Analysis ◽  
Ligand Interactions

The group of 5‐[(amidobenzyl)oxy]‐nicotinamides represents promising group of sirtuin 2 (SIRT2) inhibitors. Despite structural similarity, representatives of this group of inhibitors displayed versatile mechanisms of inhibition which hamper rational drug design. The aim of this research was to form a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model, define the pharmacophore of this subgroup of SIRT2 inhibitors, define the mode of protein-ligand interactions and design new compounds with improved predicted activity and pharmacokinetics. For the 3D-QSAR study, data set was generated using structures and activities of 166 5‐[(amidobenzyl)oxy]‐nicotinamides. 3D-conformations of compounds were optimized, alignment-independent GRIND2 descriptors were calculated and 3D-QSAR PLS models were generated using 70% of data set. To investigate bioactive conformations of inhibitors, molecular docking was used. Molecular docking analysis identified two clusters of predicted bioactive conformations which is in alignment with experimental observations. The defined pharmacophoric features were used to design novel inhibitors with improved predicted potency and ADMET profiles.

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.

Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction

2009 ◽  
Vol 63 (1) ◽  
Author(s):  
Peter Nemeček ◽  
Tatiana Ďurčeková ◽  
Ján Mocák ◽  
Karel Waisser
Keyword(s):  
Biological Activity ◽  
Chemical Shifts ◽  
Correlation Coefficients ◽  
Antimycobacterial Activity ◽  
Quantitative Structure Activity Relationship ◽  
Molar Refraction ◽  
Log P ◽  
Minimal Inhibition Concentration ◽  
Qsar Analysis ◽  
Inhibition Concentration

AbstractThis study gives a quantitative structure-activity relationship (QSAR) correlation of the 72 N-benzylsalicylamide derivatives properties with their antimycobacterial activity. The antimycobacterial activity was measured as the minimal inhibition concentration (MIC) determined for four strains of mycobacterium (M. avium, M. kansasii, M. kansasii clin.-clinically isolated form, and M. tuberculosis) after 14 days and after 21 days of cultivation. The objective was to identify the factors most closely defining biological activity of N-benzylsalicylamides, in order to enable QSAR prediction of new derivatives with high antimycobacterial activity. Optimal properties for the QSAR analysis were selected from several physicochemical properties, including lipophilicity parameter log P, molecular mass M, molar refraction MR, NMR chemical shifts, polarizability, etc. Many of the considered properties are different from those typically used in traditional QSAR. Selection of the most important properties was performed by one-way Analysis of Variance (ANOVA) and correlation analysis using the significance coefficients and the correlation coefficients, respectively. The chosen variables were further used in artificial neural networks (ANN) for predicting biological activity in the form of-log(MIC).

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