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 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 Exploring differential evolution for inverse QSAR analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1285 ◽  
Author(s):  
Tomoyuki Miyao ◽  
Kimito Funatsu ◽  
Jürgen Bajorath
Keyword(s):  
Differential Evolution ◽  
High Activity ◽  
Feature Space ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Modeling ◽  
Activity Data ◽  
Structure Generation ◽  
Qsar Analysis

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

scholarly journals Exploring differential evolution for inverse QSAR analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1285 ◽  
Author(s):  
Tomoyuki Miyao ◽  
Kimito Funatsu ◽  
Jürgen Bajorath
Keyword(s):  
Differential Evolution ◽  
High Activity ◽  
Feature Space ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Modeling ◽  
Activity Data ◽  
Structure Generation ◽  
Qsar Analysis

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

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 treatment of meisoindigo derivatives as a potentbreast anticancer agent

2019 ◽  
Vol 2 (2) ◽  
pp. 114 ◽  
Author(s):  
Evie Kama Lestari ◽  
Agus Dwi Ananto ◽  
Maulida Septiyana ◽  
Saprizal Hadisaputra
Keyword(s):  
Anticancer Activity ◽  
Anticancer Agent ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Pm3 Method ◽  
Structure Activity ◽  
Semiempirical Pm3 Method

A quantitative structure-activity relationship (QSAR) analysis of meisoindigo derivatives as a breast anticancer has been carried out. This study aimed to obtain the best QSAR model in order to design new meisoindigo based compounds with best anticancer activity. The semiempirical PM3 method was used for descriptor calculation. The best QSAR model was built using multilinear regression (MLR) with enter method. It was found that there were 19 new meisoindigo derivativeswith better predictive a potent anticancer agent. The best compound was (E)-2-(1-((3-ethylisoxazol-5-yl)methyl)-2-oxoindolin-3-ylidene)-N-(4-methoxyphenyl)acetamide with the value of IC505.31144 x10-15 (μM).

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.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

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.

QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds

2020 ◽  
Vol 17 (2) ◽  
pp. 214-225 ◽  
Author(s):  
Piotr Kawczak ◽  
Leszek Bober ◽  
Tomasz Bączek
Keyword(s):  
Antimicrobial Activity ◽  
Heterocyclic Compounds ◽  
Molecular Descriptors ◽  
Microbiological Activity ◽  
Activity Data ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Nitro Derivatives ◽  
Semi Empirical ◽  
Derivatives Of

Background: Nitro-derivatives of heterocyclic compounds were used as active agents against pathogenic microorganisms. A set of 4- and 5-nitroimidazole derivatives exhibiting antimicrobial activity was analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. The study included compounds used both in documented treatment and those described as experimental. Objective: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. Methods: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical and ab initio level of in silico molecular modeling was performed for calculations of molecular descriptors. Results: QSAR models were proposed based on chosen descriptors. The relationship between the nitro-derivatives structure and microbiological activity data was able to class and describe the antimicrobial activity with the use of statistically significant molecular descriptors. Conclusion: The applied chemometric approaches revealed the influential features of the tested structures responsible for the antimicrobial activity of studied nitro-derivatives.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In vitro Assay

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha
Keyword(s):  
Standard Error ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Vitro Assay ◽  
Physicochemical Descriptors ◽  
Qsar Models ◽  
Ic50 Values ◽  
Pharmacophore Hypotheses ◽  
Selection Of

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.

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