Coumarin structure as a lead scaffold for antibacterial agents - molecular docking

Coumarins owe their class name to “Coumarou”, the vernacular name of the tonka bean (Dipteryx odorata Willd, Fabaceae), from which coumarin was isolated in 1820. Many molecules based on the coumarin structure have been synthesized utilizing innovative synthetic techniques. Various synthetic routes have led to interesting derivatives including the furanocoumarins, pyranocoumarins and coumarinsulfamates which have been found to be useful in photochemotherapy, antitumor and anti-HIV therapy, as stimulants for central nervous system, antiinflammatory therapy, as anti-coagulants, etc. One of important pharmacological activity of coumarin molecules is their potential as antibacterial agents since they show inhibitory activity toward isoleucyl-transfer RNA (tRNA) synthetase. In the presented research molecular docking studies of selected coumarin compounds inside isoleucyltransfer RNA (tRNA) synthetase active site were performed. Molecular docking scores of all studied compounds were obtained through score functions. Presented results indicate that from all studied coumarin compounds the strongest interactions with studied enzyme has 7,8-dihydroxy-4-phenyl coumarin followed by 5,7-dihydroxy-4-phenyl coumarin. Presented results are in accordance with in vitro obtained results for their antibacterial activity. Presented findings suggest that 4-phenyl hydroxycoumarins may be considered as good molecular templates for potential antibacterial agents and can be used for further chemical modifications for improving their antibacterial activity

Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase

<div><p>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.</p></div

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

<p>Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure–activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development. Molecular docking studies are used for the final assessment of the developed QSAR model and the design of novel inhibitors. The statistical quality of the developed model is good. Molecular fragments responsible for the increase/decrease of the studied activity are calculated. The computer-aided design of new compounds, as potential ACE inhibitors, is presented. The predictive potential of the applied approach is tested, and the robustness of the model is proven using different methods. The results obtained from molecular docking studies are in excellent correlation with the results from QSAR studies. The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition.</p