Results found: 3

Number of results on page

Search results

Search:
in the keywords: Molecular descriptors

Sort By:

Limit search:

Prediction of antimicrobial activity of imidazole derivatives by artificial neural networks

100%

Wnuk M., Marszałł M., Zapęcka A., Nowaczyk A., Krysiński J., Romaszko J., Kawczak P., Bączek T., Buciński A.

Open Medicine

2013

vol. 8

issue 1

1-15

The main goal of our study is the analysis of data obtained from molecular modeling for a series of imidazole derivatives that possess strong antifungal activity. The research was designed to use artificial neural network (ANN) analysis to determine quantitative relationships between the structural parameters and anti-Streptococcus pyogenes activity of a series of imidazole derivatives. ANN in association with quantitative structure-activity relationships (QSAR) represents a promising tool in the search for drug candidates among the practically unlimited number of possible derivatives. In this work, a series of 286 imidazole derivatives presented as cationic three-dimensional structures was used. The activity was expressed as a logarithm of the reciprocal of the minimal inhibitory concentrations, log 1/MIC. Multilayer perceptron ANN was used for predictions of antimicrobial potency of new imidazole derivatives on the basis of their structural descriptors. The obtained correlation coefficient equaled 0.9461 for the learning set, 0.9060 for the validation set and 0.8824 for the testing set of imidazole derivatives. Hence, satisfactory and practically useful predictions of anti-Streptococcus pyogenes activity for a series of imidazole derivatives was obtained, supporting the future successful interpretation of QSAR analysis for those compounds.

Investigation of 6-fluoroquinolones activity against Mycobacterium tuberculosis using theoretical molecular descriptors: a case study

88%

Minovski N., Jezierska-Mazzarello A., Vračko M., Šolmajer T.

Open Chemistry

2011

vol. 9

issue 5

855-866

A quantitative structure-activity relationship (QSAR) study on a set of 66 structurally-similar 6-fluoroquinolones was performed using a large pool of theoretical molecular descriptors. Ab initio geometry optimizations were carried out to reproduce the geometrical and electronic structure parameters. The resulting molecular structures were confirmed to be minima via harmonic frequency calculations. Obtained atomic charges, HOMO and LUMO energies, orbital electron densities, dipole moment, energy and many other properties served as quantum-chemical descriptors. A multiple linear regression (MLR) technique was applied to generate a linear model for predicting the biological activity, Minimal Inhibitory Concentration (MIC), treated as negative decade logarithm, (pMIC). The heuristic method was used to optimize the model parameters and select the most significant descriptors. The model was tested internally using the CV LOO procedure on the training set and validated against the external validation set. The result (Q 2 ext = 0.7393), which was obtained on an external, previously excluded validation data set, shows the predictive performances of this model (R 2tr = 0.7416, Q 2 tr = 0.6613) in establishing (Q)SAR of 6-fluoroquinolones. This validated model could be proficiently used to design new 6-fluoroquinolones with possible higher activity. [...]

Chemometric estimation of the RP TLC retention behaviour of some estrane derivatives by using multivariate regression methods

88%

Kovačević S., Jevrić L., Podunavac Kuzmanović S., Lončar E.

Open Chemistry

2013

vol. 11

issue 12

2031-2039

Quantitative structure-retention relationship (QSRR) was developed for a series of estrane derivatives, on the basis of their retention data, obtained in reversed-phase thin-layer chromatography (RP TLC), and in silico molecular descriptors. Physicochemical and topological descriptors, as well as molecular bulkiness descriptors, were calculated from the optimized molecular structures. Full geometry optimization was achieved by using Austin Model 1 (AM1) semi-empirical molecular orbital method. In the present study, QSRR analysis was based on principal component analysis (PCA), multiple linear regression (MLR) and partial least squares (PLS) method. PCA was applied in order to reveal similarities or dissimilarities between analytes, and MLR and PLS regression methods were carried out in order to identify the most important in silico molecular descriptors and quantify their influence on the retention behaviour of studied compounds. Physically meaningful and statistically significant structure-retention relationships were established. [...]

Refine search results

2 Open Chemistry

1 Open Medicine

2 2013

1 2011

1 Buciński A.

1 Bączek T.

1 Jevrić L.

1 Jezierska-Mazzarello A.

1 Kawczak P.

1 Kovačević S.

1 Krysiński J.

1 Lončar E.

1 Marszałł M.

1 Minovski N.

1 Nowaczyk A.

1 Podunavac Kuzmanović S.

1 Romaszko J.

1 Vračko M.

1 Wnuk M.

1 Zapęcka A.

1 Šolmajer T.

Search results

Prediction of antimicrobial activity of imidazole derivatives by artificial neural networks

Investigation of 6-fluoroquinolones activity against Mycobacterium tuberculosis using theoretical molecular descriptors: a case study

Chemometric estimation of the RP TLC retention behaviour of some estrane derivatives by using multivariate regression methods