QSAR study of antimicrobial 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives using different chemometric tools (original) (raw)
Related papers
Research in Pharmaceutical Sciences, 2009
Quantitative structure-activity relationship (QSAR) studies of a series of substituted 3-hydroxy-pyridine-4-ones and 3-hydroxy-pyran-4-ones as antibacterial and antifungal agents against a variety of microorganisms were performed. Multiple linear regression approach was used as variable selection method. The antimicrobial activities of these compounds against Staphylococcus aureus, Aspergilus niger and Candida albicans were subjected to QSAR analysis. The best QSAR models were achieved for the antimicrobial activity of the studied compounds against Staphylococcus aureus and Candida albicans Quantum, constitutional and geometrical parameters had important roles in the antimicrobial activity against Staphylococcus aureus. Geometrical, functional group and topological parameters of the compounds had important effect on the antimicrobial activity against Candida albicans. The equation describing this effect had a good statistical quality (R2=0.81, SE=0.14, Q2=0.73)
European Journal of Medicinal Chemistry, 2009
A series of Mannich bases of 2-alkyl-3-hydroxy-pyridine-4-ones, namely 2-alkyl-3-hydroxy-5-N-piperidylmethyl or N,N-dialkylaminomethyl pyridine-4-ones 9, 10 and 15–18, two derivatives of N-aryl-2-methyl-3-hydroxy-pyridine-4-ones 19, 20 and two N-alkyl derivatives of maltol, 21 and 22 were prepared. They were screened for their antibacterial and antifungal activities against a variety of microorganisms using micro plate Alamar Blue® assay (MABA) method. Multiple linear regressions (MLR) analysis was performed for the synthesized compounds as well as a series of pyridinone and pyranone derivatives 23–43 which have been synthesized and evaluated for antimicrobial activity by other researchers previously. Studied compounds showed a better quantitative structure–activity relationship (QSAR) model for the antimicrobial activity against Candida albicans and Staphylococcus aureus in comparison with other tested microorganisms.
Structure-Based Classification of Antibacterial Activity
Journal of Chemical Information and Computer Sciences, 2002
The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter-and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.
2015
Quantitative structure-activity relationship (QSAR) models are useful in understanding how chemical structure relates to the biological activity of natural and synthetic chemicals and for design of newer and better therapeutics. In the present study, 15 quinolones derivatives were evaluated as antibacterial inhibitors, expre ssed by the cytotoxicity of these compounds (MIC). Based on these data, different molecular descriptors were used to solve this problem. A linear QSAR model was developed using Multiple Linear Regression technique, while Genetic Algorithm was adopted for selecting the most appropriate descriptors. The predictive activity of the model was evaluated by means of external validation set and theY-randomization technique, and its structural chemical domain has been verified by the leverage approach.
Journal of Computational Medicine, 2014
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.
Introduction. 8-hydroxyquinolin derivatives show antibacterial activity. Goal. The aim of this paper is to establish relationship between antibacterial acfivities of 8-hydroxyquinolin derivatives and their structure described by a set of electronic, steric, physicochemical and thermodynamic properties. Material and method. There were investigated 25 new compounds of S-hydroxyquinolines for which were determinate minimal inhibitory concentration (MIC, mg/dm3) against St.aureus. Results. Antibacterial activity was expressed through -lg (I/MIC) and seven descriptors was selected as independent variables influencing on the antibacterial activity. A regressional model between antibacterial activity and molecular structure descriptors was obtained. The R-squared value of the model is 0,988, which determined the model as adequate and with a high predictive ability. The model shall be helpful to predict biological activity of new sintesized compounds of this type and to outline the research for drug synthesis only to the compounds with high potential biological activity.
2012
Quantitative Structure-Activity Relationship (QSAR) studies were performed on a series of N-acyl amino acids and imidazopyrazines/pyridines derivatives with the help of PM5 calculations and geometry optimizations using Cache software. Multiple Linear Regression (MLR) analysis was performed to derive QSAR models using the descriptors, molecular weight (MW), conformation minimum energy (), HOMO energy (HOMO), solvent accessibility surface area (SASA), molar volume (MV), molar refractivity (MR), LogP (LP) and parachor (Pc). The QSAR model equations of anti-ulcer agents have been developed by using maximum of seven descriptors, in which conformation minimum energy, molar volume and parchor were present have an excellent predictive powers of correlation and cross validation coefficients. These models can successfully predict the anti-ulcer activity of any newly discovered N-acyl amino acids and imidazopyrazines/pyridines derivatives which can later be tested in laboratory.
2019
Heterocyclic molecules manelly indoles derivatives have displayed a large diversity of biological activity profiles and many studies have focused mainly their therapeutic effect. It is noticed that the biological activity solidly depends on the kind and the location of the substituents in their molecules. The Multidimensional quantitative structure-activity/property relationship (Multidimensional-QSAR/QSPR) method is used on this study, to study a series of 22 indole-pyrimidine. We have developed three-dimensional quantitative structure antifungal activity relationships for this series, using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) methods. The results show a significant statistical quality and a satisfying predictive ability, with high correlation coefficient value (R2 = 0.922 and R2=0.876 for CoMFA and CoMSIA respectively). To validate the predictive power of the resulting models, external validation multiple correl...
International Journal of Drug Discovery, 2009
A series of 5-substituted (arylmethylene) pyridin-2-amine were synthesized by condensing various 5-substituted pyridyl-2-amines with various aromatic aldehydes. The structures of newly synthesized compounds were characterized by spectral and elemental analysis. All the compounds were screened for their antibacterial activities. The QSAR studies of were performed on MOE 2006.08 software. QSAR equation reveled that selected electronic, steric and liphophillic parameters have correlation with antibacterial activity. Best equations were selected on basis of correlation coefficient (r2) and predicitivity of equation. The frequent appearance of Log P and SMR terms in the QSAR equations is indicative of lipophilic and steric parameters are the prerequisites for molecules to exhibit activity against bacteria.