Design and development of novel antibiotics based on FtsZ inhibition – in silico studies

Abstract

QSAR models, computer-aided drug design and the application of molecular docking were used to evaluate benzamide analogues as FtsZ inhibitors.

A QSAR model for predicting the corneal permeability of drugs – the application of the Monte Carlo optimization method

“…22–26 Recently, the so-called index of ideality of correlation (IIC) has been suggested as a novel criterion for the estimation of the predictive potential of QSAR models, considering not only the correlation coefficient but also the arrangement of the cluster of dots-images relative to the diagonal, in coordinates of observed–calculated values of the studied endpoint, and we calculated the IIC according to eqn (2)–(5) as the QSAR model final estimator. 27–29 Δ k = observed k − calculated k Having data on all Δ k for the test set, one can calculate sum of negative and positive values of Δ k similar to the mean absolute error (MAE):…”

In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

“…The methodology from the published paper was used entirely for this purpose (Veselinović et al 2015 ; Ojha et al 2011 ; Roy et al 2008 ), which involved the calculation of various statistical parameter values, such as the regular and cross-validated correlation coefficient, standard estimation error, mean absolute error (MAE), the Fischer ratio, root-mean-square error, R m 2 , and MAE-based metrics. What is more, the validation of the developed QSAR models in this research was achieved through data randomization (Y-scrambling test) and with the determination of concordance correlation coefficient (CCC), as well as the novel parameter, dubbed the index of ideality of correlation (IIC) (Stoičkov et al 2018 ; Toropov and Toropova 2017 ; Veselinović et al 2018 ).…”

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

“…The QSGFEAR models built in the present study utilized the SMILES based descriptors. The model for the prediction of Gibb's free energy of activation P (Δ G ‡ ) at different temperatures is computed by calculating correlation weights (CWs) of each involved attribute DCW ( T , N ) 47,48 and the one variable model is represented by eqn (2): P (Δ G ‡ ) = C 0 + C 1 × SMILES DCW( T , N epoch )where C 0 and C 1 represent the regression coefficients; T represents the optimal for the threshold to define rare (if the frequency of SMILES in the active training set (ATRN) is less than T ) and non-rare attributes (if the frequency of SMILES is greater or equal to T ) and N epoch is the optimal number of epochs of Monte Carlo optimization.…”

“…The QSGFEAR models built in the present study utilized the SMILES based descriptors. The model for the prediction of Gibb's free energy of activation P(DG ‡ ) at different temperatures is computed by calculating correlation weights (CWs) of each involved attribute DCW (T, N) 47,48 and the one variable model is represented by eqn (2):…”