Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins

Abstract

The binding of penicillins to human serum proteins was modeled with optimal descriptors based on the Simplified Molecular Input-Line Entry System (SMILES). The concentrations of protein-bound drug for 87 penicillins expressed as percentage of the total plasma concentration were used as experimental data. The Monte Carlo method was used as a computational tool to build up the quantitative structure-activity relationship (QSAR) model for penicillins binding to plasma proteins. One random data split into training, test and validation set was examined. The calculated QSAR model had the following statistical parameters: r(2) = 0.8760, q(2) = 0.8665, s = 8.94 for the training set and r(2) = 0.9812, q(2) = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r(2) = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r(2) = 0.921 and s = 7.18. SMILES-based molecular fragments (structural indicators) responsible for the increase and decrease of penicillins binding to plasma proteins were identified. The possibility of using these results for the computer-aided design of new penicillins with desired binding properties is presented.

Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials

“…The correlation weights are calculated using the Monte Carlo optimization method [12][13][14][15][16][17][18][19]. The optimization process make use of two parameters: (i) the threshold (T), which is a tool for classifying codes as either rare (and thus likely less reliable features, probably introducing noise into the model) or not rare features, which are used by the model and labeled as active; and (ii) the number of epochs (N), which is the number of cycles (sequence of modifications of correlation weights for all codes involved in model development) for the optimization [15][16][17][18].…”

“…Building up predictive models for endpoints related to nanomaterials is an important task of modern natural sciences [4]. Likely, the traditional quantitative structureproperty / activity relationships (QSPRs/QSARs) [5][6][7][8][9][10][11][12][13] based on the molecular structure are not able to solve this task.…”

Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors

“…Several studies [34,35,37,38,41] used these validation methodologies on SMILES notation optimal descriptor based QSAR models. In this respect the cross-validated correlation coefficient (Q 2 ) and error of estimation (s) were calculated based on predicted activity of training compounds.…”

Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors

“…Test set Monte Carlo optimization runs is positive then that SA k is the promoter of increase; if CW(SA k ) from three independent Monte Carlo optimization runs is negative then that SA k is the promoter of decrease; if there are both positive and negative values of CW (Sk) in three runs of the Monte Carlo optimization process, then that SA k has an undefined role [26][27][28][29]32,33]. The list of all SA k , with the correlation weights for three runs of the Monte Carlo optimization process of the built QSAR model for maleimide derivatives is shown in Table S3.…”

“…QSAR modeling is often based on optimal descriptors calculated with the molecular graph [26][27][28]. The simplified molecular input-line entry system (SMILES) can be considered as an alternative for the representation of the molecular structure by the molecular graph [29][30][31]. A SMILES notation based optimal descriptor is a molecular descriptor which depends both on the molecular structure and the property under analysis, but does not explicitly depend on details from the 3D-molecular geometry.…”