Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition-In silico approach.

Rho kinases, one of the best-known members of the serine/threonine (Ser/Thr) protein kinase family, can be used as target enzymes for the treatment of many diseases such as cancer or multiple sclerosis, and especially for the treatment of cardiovascular diseases. This study presents QSAR modeling for a series of 41 chemical compounds as Rho kinase inhibitors based on the Monte Carlo method. QSAR models were developed for three random splits into the training and test set. Molecular descriptors used for QSAR modeling were based on the SMILES notation and local invariants of the molecular graph. The statistical quality of the developed model, including robustness and predictability, was tested with different statistical approaches and satisfying results were obtained. The best calculated QSAR model had the following statistical parameters: r2 = 0.8825 and q2 = 0.8626 for the training set and r2 = 0.9377 and q2 = 0.9124 for the test set. Novel statistical metric entitled as the index of ideality of correlation was used for the final model assessment, and the obtained results were 0.6631 for the training and 0.9683 for the test set. Molecular fragments responsible for the increases and decreases of the studied activity were defined and they were further used for the computer-aided design of new compounds as potential Rho kinase inhibitors. The final assessment of the developed QSAR model and designed inhibitors was achieved with the application of molecular docking. An excellent correlation between the results from QSAR and molecular docking studies was obtained.

In silico studies and the design of novel agents for the treatment of systemic tuberculosis.

Tuberculosis (TB) is an ancient infectious disease, which re-emerged with the appearance of multidrug-resistant strains and acquired immune deficiency syndrome. Enoyl-acyl-carrier protein reductase (InhA) has emerged as a promising target for the development of anti-tuberculosis therapeutics. This study aims to develop quantitative structure-activity relationship (QSAR) models for a series of arylcarboxamides as InhA inhibitors. The QSAR models were calculated on the basis of optimal molecular descriptors based on the simplified molecular-input line-entry system (SMILES) notation with the Monte Carlo method as a model developer. The molecular docking study was used for the final assessment of the developed QSAR model and designed novel inhibitors. Methods used for the validation indicated that the predictability of the developed model was good. Structural indicators defined as molecular fragments responsible for increases and decreases of the studied activity were defined. The computer-aided design of new compounds as potential InhA inhibitors was presented. The Monte Carlo optimization was capable of being an efficient in silico tool for developing a model of good statistical quality. The predictive potential of the applied approach was tested and the robustness of the model was proven using different methods. The results obtained from molecular docking studies were in excellent correlation with the results from QSAR studies. This study can be useful in the search for novel anti-tuberculosis therapeutics based on InhA inhibition. Communicated by Ramaswamy H. Sarma.

The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study.

Up to this date, there has been an ongoing debate about the mode of action of general anesthetics, which have postulated many biological sites as targets for their action. However, postoperative nausea and vomiting are common problems in which inhalational agents may have a role in their development. When a mode of action is unknown, QSAR modelling is essential in drug development. To investigate the aspects of their anesthetic, QSAR models based on the Monte Carlo method were developed for a set of polyhalogenated ethers. Until now, their anesthetic action has not been completely defined, although some hypotheses have been suggested. Therefore, a QSAR model should be developed on molecular fragments that contribute to anesthetic action. QSAR models were built on the basis of optimal molecular descriptors based on the SMILES notation and local graph invariants, whereas the Monte Carlo optimization method with three random splits into the training and test set was applied for model development. Different methods, including novel Index of ideality correlation, were applied for the determination of the robustness of the model and its predictive potential. The Monte Carlo optimization process was capable of being an efficient in silico tool for building up a robust model of good statistical quality. Molecular fragments which have both positive and negative influence on anesthetic action were determined. The presented study can be useful in the search for novel anesthetics.

QSPR in forensic analysis - The prediction of retention time of pesticide residues based on the Monte Carlo method.

A method for the prediction of retention indices of pesticides using the Monte Carlo method and with optimal molecular descriptors based on local graph invariants and the SMILES notation of studied compounds has been presented. Quite satisfactory results were obtained with the proposed method, since a robust model with good statistical quality was developed. The predictive potential of the applied approach was tested and the robustness of the model was proven with different methods. The best calculated QSPR model had following statistical parameters: r2 = 0.9182 for the training set and r2 = 0.8939 for the test set. Structural indicators defined as molecular fragments responsible for the increases and decreases of gas chromatographic retention indices activity were calculated.