2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against <i>Pseudomonas aeruginosa</i> and <i>Staphylococcus aureus</i>: A Comparative Approach

Datar, Prasanna A.

doi:10.1155/2014/765457

Cited by 14 publications

(7 citation statements)

References 20 publications

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“…Pseudomonas aeruginosa is one of the six ESKAPE microbial species that are justifiably worrying for the public health landscape today [ 4 ] and a worldwide need to develop new antibiotics active against this bacterial species is increasingly pressing [ 7 ]. In the available literature, QSAR models have been reported for substances potentially active against P. aeruginosa: local 2D-QSAR models for specific chemical classes (indolylpyrimidines [ 26 ], N-octaneamino-4-aminoquinolines [ 27 ]), local 2-D [ 28 ] and 3D-QSAR models of compounds active against specific protein targets from P. aeruginosa , such as the UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) [ 29 ]. A multi-tasking QSAR model (oriented to both predicting anti- Pseudomonas activities and ADMET properties of chemical compounds) has also been developed [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…Molecules 2021, 26, 1734 2 of 18 The introduction of antibiotics in therapy has marked unprecedented progress in the control of disease and reduction of mortality in human history, conservative estimates indicating death reductions by 25-75% for different diseases [8]. The development of new antibiotics has remained a challenge in the last decades, with low yields despite impressive progresses in certain areas of drug development [9].…”

Section: Introductionmentioning

confidence: 99%

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QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

2021

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Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

2021

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“…Moreover, the predictive power of the QSAR model was verified using LOO internal validation or cross validation (q 2 ). Usually, a value of q 2 > 0.5 is considered acceptable [26]. In this model, the r 2 was 0.732, r 2 pred was 0.613, and q 2 equaled 0.562, which indicated the true predictive ability of the model (Table 3) The predicted activities for the inhibitors versus their experimental activities and the residues between them are listed in Table 1.…”

Section: Introductionmentioning

confidence: 92%

QSAR, Docking, and Molecular Dynamics Simulation Studies of Sigmacidins as Antimicrobials against Streptococci

Yang

2022

IJMS

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Streptococci are a family of bacterial species significantly affecting human health. In addition, environmental Streptococci represent one of the major causes of diverse livestock diseases. Due to antimicrobial resistance, there is an urgent need for novel antimicrobial agent discovery against Streptococci. We discovered a class of benzoic acid derivatives named sigmacidins inhibiting the bacterial RNA polymerase-σ factor interaction and demonstrating excellent antimicrobial activity against Streptococci. In this work, a combinational computer approach was applied to gain insight into the structural basis and mechanism of action of sigmacidins as antimicrobials against Streptococcus pneumoniae. Both two- and three-dimensional quantitative structure-active relationships (2D and 3D QSAR) of sigmacidins displayed good predictive ability. Moreover, molecular docking and molecular dynamics simulation studies disclosed possible contacts between the inhibitors and the protein. The results obtained in this study provided understanding and new directions to the further optimizations of sigmacidins as novel antimicrobials.

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“…Descriptors map the chemical structure into a set of binary/ numerical values illustrating numerous molecular properties essential for explaining molecular property/ activity 71 . Descriptors are classified into different properties such as electronic, geometric, hydrophobic, and topological 72 .…”

Section: Molecular Descriptors Calculationmentioning

confidence: 99%

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2021

IJPSR

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Cathepsin S enzyme has been considered as an evolving target for the development of novel therapeutic agents for the treatment of numerous autoimmune disorders and other inflammatory diseases. Using TSAR 3.3 2D QSAR has been performed on a series of dipeptide nitrile nucleus. Attempts have been made to derive and comprehend a correlation between biological activity and the generated descriptors. The study was carried out using 37 compounds by division into training and test set by a random selection method. A final QSAR model was generated from a set of 28 compounds with the Leave-out one row (LOO) method of crossvalidation to estimate the model's predictive ability. The most significant model with n = 28, r = 0.969, r 2 = 0.939, r 2 cv = 0.801, s value = 0.35, f value = 89.07 was developed using MLR analysis. For PLS, the fraction of variance explained = 0.928 was observed. A comparable PLS model with r 2 = 0.928 and Neural model with r 2 = 0.962 indicated good internal predictability of the model. External test set validation provided r 2 values of 0.721 and 0.821 for MLR and PLS analysis, respectively. QSAR model indicated the importance of Steric [Verloop B1 (Subs. 4)], Geometrical [Inertia moment 1 length (Subs. 4), topological [kier Chi V0 (atoms) index (Subs. 2)], and [Kier Chi 4 (path) index (Subs.4

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

Cited by 14 publications

References 20 publications

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

QSAR, Docking, and Molecular Dynamics Simulation Studies of Sigmacidins as Antimicrobials against Streptococci

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