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 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 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).

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

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.

Design of Dual COX-2 and 5-LOX Inhibitors with Iron-Chelating Properties Using Structure-Based and Ligand-Based Methods

2021 ◽  
Vol 18 ◽  
Author(s):  
Jelena Bošković ◽  
Dušan Ružić ◽  
Olivera Čudina ◽  
Katarina Nikolic ◽  
Vladimir Dobričić
Keyword(s):  
Molecular Docking ◽  
External Validation ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Qsar Analysis ◽  
In Silico Admet ◽  
Cox 2 ◽  
Lox Inhibitors

Background: Inflammation is common pathogenesis of many diseases progression, such as malignancy, cardiovascular and rheumatic diseases. The inhibition of the synthesis of inflammatory mediators by modulation of cyclooxygenase (COX) and lipoxygenase (LOX) pathways provides a challenging strategy for the development of more effective drugs. Objective: The aim of this study was to design dual COX-2 and 5-LOX inhibitors with iron-chelating properties using a combination of ligand-based (three-dimensional quantitative structure-activity relationship (3D-QSAR)) and structure-based (molecular docking) methods. Methods: The 3D-QSAR analysis was applied on a literature dataset consisting of 28 dual COX-2 and 5-LOX inhibitors in Pentacle software. The quality of developed COX-2 and 5-LOX 3D-QSAR models were evaluated by internal and external validation methods. The molecular docking analysis was performed in GOLD software, while selected ADMET properties were predicted in ADMET predictor software. Results: According to the molecular docking studies, the class of sulfohydroxamic acid analogues, previously designed by 3D-QSAR, was clustered as potential dual COX-2 and 5-LOX inhibitors with iron-chelating properties. Based on the 3D-QSAR and molecular docking, 1j, 1g, and 1l were selected as the most promising dual COX-2 and 5-LOX inhibitors. According to the in silico ADMET predictions, all compounds had an ADMET_Risk score less than 7 and a CYP_Risk score lower than 2.5. Designed compounds were not estimated as hERG inhibitors, and 1j had improved intrinsic solubility (8.704) in comparison to the dataset compounds (0.411-7.946). Conclusion: By combining 3D-QSAR and molecular docking, three compounds (1j, 1g, and 1l) are selected as the most promising designed dual COX-2 and 5-LOX inhibitors, for which good activity, as well as favourable ADMET properties and toxicity, are expected.

scholarly journals Quantitative Structure Activity Relationship (QSAR) Based on Electronic Descriptors and Docking Studies of Quinazoline Derivatives for Anticancer Activity

2018 ◽  
Vol 34 (5) ◽  
pp. 2361-2369
Author(s):  
Herlina Rasyid ◽  
Bambang Purwono ◽  
Ria Armunanto
Keyword(s):  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Docking Studies ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity ◽  
B3lyp Method ◽  
Quinazoline Derivatives

Quantitative structure-activity relationship (QSAR) based on electronic descriptors had been conducted on 2,3-dihydro-[1,4]dioxino[2,3-f]quinazoline analogues as anticancer using DFT/B3LYP method. The best QSAR equation described as follow: Log IC50 = -11.688 + (-35.522×qC6) + (-21.055×qC10) + (-85.682×qC12) + (-32.997×qO22) + (-85.129 EHOMO) + (19.724×ELUMO). Statistical value of R2 = 0.8732, rm2 = 0.7935, r2-r02/r2 = 0.0118, PRESS = 1.5727 and Fcalc/Ftable = 2.4067 used as external validation. Atomic net charge showed as the most important descriptor to predict activity and design new molecule. Following QSAR analysis, Lipinski rules was applied to filter the design compound due to physicochemical properties and resulted that all filtered compounds did not violate the rules. Docking analysis was conducted to determine interaction between proposed compounds and EGFR protein. Critical hydrogen bond was found in Met769 residue suggesting that proposed compounds could be used to inhibit EGFR protein.

scholarly journals QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

2014 ◽  
Vol 79 (9) ◽  
pp. 1111-1125 ◽  
Author(s):  
Dan-Dan Wang ◽  
Lin-Lin Feng ◽  
Guang-Yu He ◽  
Hai-Qun Chen
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Tetrahymena Pyriformis ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Qsar Studies ◽  
Qsar Models ◽  
Leave One Out

Quantitative structure-activity relationship (QSAR) models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS) was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl?Cl), Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR) method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944). Moreover, Y-scrambling test indicated there was no chance correlation in this model.

QSAR Analysis of Saquinavir Analogues (Peptidomimetics) as Potent HIV‐Protease Inhibitors

2012 ◽  
Vol 2 (3) ◽  
pp. 118-127
Author(s):  
Vandana Saini ◽  
Ajit Kumar
Keyword(s):  
Biological Activity ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Partial Least Square ◽  
Least Square ◽  
Activity Prediction ◽  
Qsar Analysis ◽  
Qsar Studies ◽  
Physicochemical Descriptors ◽  
Qsar Models

The correlation of structural features with the biological activity has always played an important role in drug designing process. The present paper discussesthe 2D‐ and 3D‐ Quantitative structure activity relationship (QSAR) studies, performed on a series of compounds related to saquinavir, an established HIV‐protease inhibitor (PI). The analysis was done on structure based calculations using various methods of QSAR like multiple linear regression (MLR), k‐nearest neighbour (k‐NN) and partial least square (PLS), to establish QSAR models for biological activity prediction of unknown compounds. A total of 27 peptidomimetics (Saquinavir analogues) were used for the study and models were developed using a training set of 22 compounds and test set of 5 compounds. The r2 value of 0.959 and crossvalidated r2 (q2) of 0.926 was obtained when models were generated using physicochemical descriptors during 2D‐QSAR analysis. In case of 3D‐QSAR analysis, database alignment of all compounds was done by field fit of steric and electrostatic molecular fields. 3D‐QSAR models generated showed r2 of 0.81 when steric and electrostatic fields were considered as basis of model generation. The meaningful information obtained from the study can be used for the design of saquinavir analogues having better inhibitory activity for HIV‐protease. Also, the QSAR models generated can be very useful to predict the HIV‐PIs and also for virtual screening for identification of new lead molecules.

Chemometric Modeling of the Ecotoxicity of Industrial Chemicals to an Avian Species Anas Platyrhynchos

2020 ◽  
Vol 5 (2) ◽  
pp. 1-16
Author(s):  
Shinjita Ghosh ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Anas Platyrhynchos ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Avian Species ◽  
Industrial Chemicals ◽  
Linear Discriminant ◽  
Topological Distance ◽  
Qsar Models ◽  
Classification And Regression

Birds or avians have been imperative species in the ecology, having been evaluated in an effort to understand the toxic effects of endocrine disruption. The ecotoxicity of 56 industrial chemicals classified as endocrine disruptors were modeled employing classification and regression-based quantitative structure-activity relationship (QSAR) models to an important avian species, Anas platyrhynchos. The classification- and regression-based QSAR models were developed using linear discriminant analysis (LDA) and partial least squares (PLS) tools, respectively. All models were validated meticulously by employing internal and external validation metrics followed by randomization test, applicability domain (AD) study, and intelligent consensus prediction of all individual models. Features like topological distance of 1, 3, and 5 between atoms O-P, C-P, and N-S, correspondingly, along with the CR3X fragment, can be responsible for an increase in toxicity. On the contrary, the presence of S-Cl with topological distance 6 is accountable for lowering the toxicity of towards A. platyrhynchos. The developed chemometric models can offer significant evidence and guidance in the framework of virtual screening as well as a toxicity prediction of new and/or untested chemical libraries towards this specific avian species.

High-accuracy QSAR models of narcosis toxicities of phenols based on various data partition, descriptor selection and modelling methods

RSC Advances ◽  
2016 ◽  
Vol 6 (108) ◽  
pp. 106847-106855 ◽  
Author(s):  
Wei Zhou ◽  
Yanjun Fan ◽  
Xunhui Cai ◽  
Yan Xiang ◽  
Peng Jiang ◽  
...  
Keyword(s):  
Environmental Protection Agency ◽  
Quantitative Structure Activity Relationship ◽  
Toxicity Tests ◽  
Data Partition ◽  
Protection Agency ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Descriptor Selection ◽  
Qsar Models ◽  
Modelling Methods

The environmental protection agency thinks that quantitative structure–activity relationship (QSAR) analysis can better replace toxicity tests.

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