Aleksandar M. Veselinović

SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL

A predictive quantitative structure - activity relationships model of arylpiperazines as high-affinity 5-HT(1A) receptor ligands was developed using CORAL software (http://www.insilico.eu/CORAL). Simplified molecular input-line entry system (SMILES) was used as representation of the molecular structure of the arylpiperazines. The balance of correlations was used in the Monte Carlo optimization aimed to build up optimal descriptors for one-variable models. The robustness of this model has been tested in four random splits into the sub-training, calibration, and test set. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r²) for the test sets of the four random splits are 0.9459, 0.9249, 0.9473 and 0.9362.

SMILES‐Based QSAR Models for the Calcium Channel‐Antagonistic Effect of 1,4‐Dihydropyridines

The activity of 72 1,4‐dihydropyridines as calcium channel antagonists was examined. The simplified molecular input‐line entry system (SMILES) was used as representation of the molecular structure of the calcium channel antagonists. Quantitative structure–activity relationships (QSARs) were developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the training and test sets. Using the Monte Carlo method, the CORAL software generated the optimal descriptors for one‐variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The obtained results reveal good predictive potential of the applied approach: The correlation coefficients (r2) for the test sets of the four random splits are 0.9571, 0.9644, 0.9836, and 0.9444.

QSAR as a random event: a case of NOAEL

Quantitative structure–activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the “visible” training set and “invisible” validation. The statistical quality of the models for three random splits are the following: split 1 n = 180, r2 = 0.718, q2 = 0.712, s = 0.403, F = 454 (training set); n = 17, r2 = 0.544, s = 0.367 (calibration set); n = 21, r2 = 0.61, s = 0.44, rm2 = 0.61 (validation set); split 2 n = 169, r2 = 0.711, q2 = 0.705, s = 0.409, F = 411 (training set); n = 27, r2 = 0.512, s = 0.461 (calibration set); n = 22, r2 = 0.669, s = 0.360, rm2 = 0.63 (validation set); split 3 n = 172, r2 = 0.679, q2 = 0.672, s = 0.420, F = 360 (training set); n = 19, r2 = 0.617, s = 0.582 (calibration set); n = 21, r2 = 0.627, s = 0.367, rm2 = 0.54 (validation set). All models are built according to OCED principles.

Antioxidant properties of selected 4-phenyl hydroxycoumarins: Integrated in vitro and computational studies.

A study on the structure-activity relationship of three hydroxy 4-phenyl coumarins, carried out by employing a series of different chemical cell-free tests is presented. Different assays involving one redox reaction with the oxidant (DPPH, ABTS, FRAP and CUPRAC) were employed. Further, the measurement of inhibition of oxidative degradation, such as lipid peroxidation, was used to define compound antioxidant activity. Our results confirm the good antioxidant activity of the 7,8-dihydroxy-4-phenyl coumarin and moderate antioxidant activity of 5,7-dihydroxy-4-phenyl coumarin. In this work, quantum chemical calculations based on density functional theory have been employed at B3LYP/6-311++G(d,p) level of theory to study the influence of number and position of hydroxyl groups in coumarin molecules on antioxidant activity. Calculated values for HOMO and LUMO energies, energy gap, stabilization energies and spin density distribution confirmed experimental results and were used for SAR definition. For determination of reaction mechanism in gas phase and selected solvents bond dissociation enthalpy, adiabatic ionization potential, proton dissociation enthalpy, proton affinity, electron transfer enthalpy and gas phase acidity have been calculated. Hydrogen Atom Transfer mechanism in vacuum and Single-Electron Transfer followed by the Proton Transfer mechanism in other studied systems are most probable free radical scavenging pathways. On the basis of these findings, these hydroxy 4-phenyl coumarins may be considered as potential therapeutic candidates for pathological conditions characterized by free radical overproduction.

Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase

A series of 278 organophosphate compounds acting as acetylcholinesterase inhibitors has been studied. The Monte Carlo method was used as a tool for building up one-variable quantitative structure–activity relationship (QSAR) models for acetylcholinesterase inhibition activity based on the principle that the target endpoint is treated as a random event. As an activity, bimolecular rate constants were used. The QSAR models were based on optimal descriptors obtained from Simplified Molecular Input-Line Entry System (SMILES) used for the representation of molecular structure. Two modelling approaches were examined: (1) ‘classic’ training-test system where the QSAR model was built with one random split into a training, test and validation set; and (2) the correlation balance based QSAR models were built with two random splits into a sub-training, calibration, test and validation set. The DModX method was used for defining the applicability domain. The obtained results suggest that studied activity can be determined with the application of QSAR models calculated with the Monte Carlo method since the statistical quality of all build models was very good. Finally, structural indicators for the increase and the decrease of the bimolecular rate constant are defined. The possibility of using these results for the computer-aided design of new organophosphate compounds is presented.

Synthesis and Evaluation of Series of Diazine-Bridged Dinuclear Platinum(II) Complexes through in Vitro Toxicity and Molecular Modeling: Correlation between Structure and Activity of Pt(II) Complexes

Polynuclear Pt(II) complexes are a novel class of promising anticancer agents with potential clinical significance. A series of pyrazine (pz) bridged dinuclear Pt(II) complexes with general formulas {[Pt(L)Cl]2(μ-pz)}(2+) (L, ethylenediamine, en; (±)-1,2-propylenediamine, 1,2-pn; isobutylenediamine, ibn; trans-(±)-1,2-diaminocyclohexane, dach; 1,3-propylenediamine, 1,3-pd; 2,2-dimethyl-1,3-propylenediamine, 2,2-diMe-1,3-pd) and one pyridazine (pydz) bridged {[Pt(en)Cl]2(μ-pydz)}(2+) complex were prepared. The anticancer potential of these complexes were determined through in vitro cytotoxicity assay in human fibroblasts (MRC5) and two carcinoma cell lines (A375 and HCT116), interaction with double stranded DNA through in vitro assay, and molecular docking study. All complexes inhibited cell proliferation with inhibitory concentrations in the 0.5-120 μM range. While {[Pt(1,3-pd)Cl]2(μ-pz)}(2+) showed improved activity and {[Pt(en)Cl]2(μ-pydz)}(2+) showed comparable activity to that of clinically relevant cisplatin, {[Pt(en)Cl]2(μ-pydz)}(2+) was less toxic in an assay with zebrafish (Danio rerio) embryos, causing no adverse developmental effects. The in vitro cytotoxicity of all diazine-bridged dinuclear Pt(II) complexes is discussed in correlation to their structural characteristics.

QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids

Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization. In this work, the representation of the molecular structure of peptides (mastoparan analogs) by a sequence of amino acids has been used to establish quantitative structure-activity relationships (QSARs) for their antibacterial activity. The data for the studied peptides were split three times into the training, calibration and test sets. The Monte Carlo method was used as a computational technique for QSAR models calculation. The statistical quality of QSAR for the antibacterial activity of peptides for the external validation set was: n=7, r(2)=0.8067, s=0.248 (split 1); n=6, r(2)=0.8319, s=0.169 (split 2); and n=6, r(2)=0.6996, s=0.297 (split 3). The stated statistical parameters favor the presented QSAR models in comparison to 2D and 3D descriptor based ones. The Monte Carlo method gave a reasonably good prediction for the antibacterial activity of peptides. The statistical quality of the prediction is different for three random splits. However, the predictive potential is reasonably well for all cases. The presented QSAR modeling approach can be an attractive alternative of 3D QSAR at least for the described peptides.

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

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: r2 = 0.8760, q2 = 0.8665, s = 8.94 for the training set and r2 = 0.9812, q2 = 0.9753, s = 7.31 for the test set. For the validation set, the statistical parameters were r2 = 0.727 and s = 12.52, but after removing the three worst outliers, the statistical parameters improved to r2 = 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.

Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions

The experimental data on the bacterial reverse mutation test (under various conditions) on C60 nanoparticles for the cases (i) TA100, and (ii) WP2uvrA/pkM101 are examined as endpoints. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of these endpoints has been built up. The models are a mathematical function of eclectic data such as (i) dose (g/plate); (ii) metabolic activation (i.e. with mix S9 or without mix S9); and (iii) illumination (i.e. darkness or irradiation). The eclectic data on different conditions were represented by so-called quasi-SMILES. In contrast to the traditional SMILES which are representation of molecular structure, the quasi-SMILES are representation of conditions by sequence of symbols. The calculations were carried out with the CORAL software, available on the Internet at http://www.insilico.eu/coral. The main idea of the suggested descriptors is the accumulation of all available eclectic information in the role of logical and digital basis for building up a model. The computational experiments have shown that the described approach can be a tool to build up models of mutagenicity of fullerene under different conditions.

In silico prediction of the β-cyclodextrin complexation based on Monte Carlo method.

In this study QSPR models were developed to predict the complexation of structurally diverse compounds with β-cyclodextrin based on SMILES notation optimal descriptors using Monte Carlo method. The predictive potential of the applied approach was tested with three random splits into the sub-training, calibration, test and validation sets and with different statistical methods. Obtained results demonstrate that Monte Carlo method based modeling is a very promising computational method in the QSPR studies for predicting the complexation of structurally diverse compounds with β-cyclodextrin. The SMILES attributes (structural features both local and global), defined as molecular fragments, which are promoters of the increase/decrease of molecular binding constants were identified. These structural features were correlated to the complexation process and their identification helped to improve the understanding for the complexation mechanisms of the host molecules.