New Advances in QSPR/QSAR Analysis of Nitrocompounds: Solubility, Lipophilicity, and Toxicity

2012 ◽  
pp. 279-334
Author(s):  
Liudmyla N. Ognichenko ◽  
Victor E. Kuz’min ◽  
Leonid Gorb ◽  
Eugene N. Muratov ◽  
Anatoly G. Artemenko ◽  
...  
Keyword(s):  
Qsar Analysis

DESIGN OF NEW PEROXISOME PROLIFERATORS GAMMA ACTIVATED RECEPTOR AGONISTS (PPARγ) VIA QSAR BASED MODELING

2018 ◽  
pp. 23-26 ◽  
Author(s):  
Tripathi RB ◽  
Jain J ◽  
Siddiqui AW
Keyword(s):  
Molecular Descriptor ◽  
Binding Pocket ◽  
Peroxisome Proliferators ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Artery Disease ◽  
Peroxisome Proliferators Activated Receptors ◽  
Recent Attention

The Peroxisome proliferators-activated receptors (PPARs) are one of the nuclear fatty acid receptors, which contain a type II zincfinger DNA binding pattern and a hydrophobic ligand binding pocket. These receptors are thought to play an essential role in metabolic diseasessuch as obesity, insulin resistance, and coronary artery disease. Therefore Peroxisome Proliferators-Activated Receptor (PPARγ) activators havedrawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize newPPARγ activators. Objective: The aim of the study was to finding new selective human PPARγ (PPARγ) modulators that are able to improveglucose homeostasis with reduced side effects compared with TZDs and identify the specific molecular descriptor and structural constraint toimprove the agonist activity of PPARγ analogs. Material and Method: Software’s that was used for this study include S.P. Gupta QSARsoftware (QSAR analysis), Valstat (Comparative QSAR analysis and calculation of L-O-O, Q2, r2, Spress), BILIN (Comparative QSAR analysisand calculation of Q2, r, S, Spress, and F), etc., allowing directly performing statistical analysis. Then multiple linear regression based QSARsoftware (received from BITS-Pilani, India) generates QSAR equations. Result and Discussion: In this study, we explored the quantitativestructure–activity relationship (QSAR) study of a series of meta-substituted Phenyl-propanoic acids as Peroxisome Proliferators Gamma activatedreceptor agonists (PPARγ).The activities of meta-substituted Phenyl-propanoic acids derivatives correlated with various physicochemical, electronic and steric parameters.Conclusion: The identified QSAR models highlighted the significance of molar refractivity and hydrophobicity to the biological activity.

Alignment-independent QSAR Analysis of SecA Inhibitors

2013 ◽  
Vol 20 (7) ◽  
pp. 802-807 ◽  
Author(s):  
Min Xu ◽  
Yiling Bi ◽  
Mengyuan Zhu ◽  
Minyong Li
Keyword(s):  
Qsar Analysis

In vitro Cytotoxicity and Genotoxicity Analysis of Ten Tannery Chemicals Using SOS/umu Tests and High-content In vitro Micronucleus Tests

2018 ◽  
Vol 21 (4) ◽  
pp. 262-270 ◽  
Author(s):  
Zehao Huang ◽  
Na Li ◽  
Kaifeng Rao ◽  
Cuiting Liu ◽  
Zijian Wang ◽  
...  
Keyword(s):  
Micronucleus Test ◽  
A549 Cells ◽  
Quantitative Structure Activity Relationship ◽  
In Vitro Cytotoxicity ◽  
Cytotoxic Effects ◽  
Qsar Analysis ◽  
Cell Assays ◽  
Micronucleus Tests ◽  
Damaging Effects

Background: More than 2,000 chemicals have been used in the tannery industry. Although some tannery chemicals have been reported to have harmful effects on both human health and the environment, only a few have been subjected to genotoxicity and cytotoxicity evaluations. Objective: This study focused on cytotoxicity and genotoxicity of ten tannery chemicals widely used in China. Materials and Methods: DNA-damaging effects were measured using the SOS/umu test with Salmonella typhimurium TA1535/pSK1002. Chromosome-damaging and cytotoxic effects were determined with the high-content in vitro Micronucleus test (MN test) using the human-derived cell lines MGC-803 and A549. Conclusion: The cytotoxicity of the ten tannery chemicals differed somewhat between the two cell assays, with A549 cells being more sensitive than MGC-803 cells. None of the chemicals induced DNA damage before metabolism, but one was found to have DNA-damaging effects on metabolism. Four of the chemicals, DY64, SB1, DB71 and RR120, were found to have chromosome-damaging effects. A Quantitative Structure-Activity Relationship (QSAR) analysis indicated that one structural feature favouring chemical genotoxicity, Hacceptor-path3-Hacceptor, may contribute to the chromosome-damaging effects of the four MN-test-positive chemicals.

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.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

3D-QSAR Analysis on N-Phenylacetanoyl-L-Homoserine Lactones as Inhibitors of Bacterial Quorum Sensing via CoMFA Approach

2008 ◽  
Vol 5 (7) ◽  
pp. 449-453
Author(s):  
Ping-Hua Sun ◽  
Zhao-Qi Yang ◽  
Wei-Min Chen ◽  
Qian Liu ◽  
Xin-Sheng Yao
Keyword(s):  
Quorum Sensing ◽  
3D Qsar ◽  
Homoserine Lactones ◽  
Qsar Analysis ◽  
Bacterial Quorum Sensing ◽  
Bacterial Quorum

A QSAR Analysis of Coumarin Derivatives as TNF-α Inhibitor - A Rational Approach to Anticancer Drug Design

2011 ◽  
Vol 8 (9) ◽  
pp. 868-876 ◽  
Author(s):  
Malleshappa N. Noolvi ◽  
Harun M. Patel ◽  
Tarandeep Kaur
Keyword(s):  
Drug Design ◽  
Anticancer Drug ◽  
Rational Approach ◽  
Coumarin Derivatives ◽  
Qsar Analysis

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.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

2019 ◽  
Vol 15 (6) ◽  
pp. 588-601 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Rua'a A. Al-Aqtash ◽  
Mutasem O. Taha
Keyword(s):  
Virtual Screening ◽  
Inflammatory Diseases ◽  
Qsar Model ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Physicochemical Descriptors ◽  
Qsar Modelling ◽  
Ic50 Values ◽  
Phosphoinositide 3 Kinase ◽  
Pharmacophore Models

<P>Background: PI3K&#948; is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3K&#948; inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3K&#948; binding. </P><P> Methods: Seventeen PI3K&#948; crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). </P><P> Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. </P><P> Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3K&#948; inhibitors.</P>

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