QSAR of Flavonoids: 4. Differential Inhibition of Aldose Reductase and p56lck Protein Tyrosine Kinase

Flavonoids are a group of low molecular weight plant products, based on the parent compound, flavone (2-phenylchromone) and have shown potential for application in a variety of pharmacological tar-gets. By using random screening techniques flavones have been proposed as inhibitors of aldose reductase, an enzyme crucial in the treatment of diabetic complications such as cataract formation. On the other hand, a large number of natural and synthetic flavonoids are being tested as specific inhibitors of protein tyrosine kinase (PTK). Kinetic analyses of the PTK inhibition indicate that flavonoids are competitive inhibitors with respect to the nucleotide ATP A thorough investigation of the available experimental data base by using both classical and quantum Chemical descriptors has been performed in order to develop quantitative structure-activity relationships for these enzyme systems. Relevance of the descriptors to binding properties of both enzyme receptors active site is proposed and the obtained results demonstrate in detail which specific electronic as well as the hydrophobic and steric properties of the substituents play a significant role in their differential binding

Quantitative Structure-Activity Relationship of Tricyclic Carbapenems: Application of Artificial Intelligence Methods for Bioactivity Prediction

Resistance to antibiotics in bacterial population has widened the interest of Scientific community for development of novel therapeutic compounds. Penicillins and cephalosporins which share the β-lactam structural moiety form the most abundant group of antibiotics on the market. Their recently developed tricyclic analogues have shown remarkable bioactivity towards broad spectrum of bacterial species. In a series of 52 tricyclic carbapenems represented by the 180’dimensional »spectrum-like« representation we studied the structure-activity relationships by application of an artificial neural network. The molecular structure representation by spec-tral intensity values served as inputs into the counter-propagation artificial neural network (CP-ANN). SIMPLEX optimization was carried out to obtain the best ANN model and a genetic algorithm approach was subsequently used to simultaneously minimize the number of variables. Thus, a search for the substituents that predominantly influence the experimental bioactivity was performed. The constructed CP-ANN model yielded bioactivity values predictions with a correlation coefficient of 0.88, with their values extended over 4 orders of magnitude. The list of substituents selected by our automatic procedure can be compared with the data obtained by protein crystallography of the β-lactam inhibitors in complex with D,D-peptidase enzyme

Chemometrical Exploration of Combinatorially Generated Drug-like Space of 6-fluoroquinolone Analogs: A QSAR Study

<p>A classical virtual combinatorial chemistry approach (CombiChem) was applied for combinatorial generation of 5590 novel structurally-similar 6-fluoroquinolone analogs by using a virtual synthetic pathway with selected primary (43) and secondary amines (130). The obtained virtual combinatorial library was filtered using an in-house developed set of cheminformatics drug-likeness filters with pre-integrated Boolean options (TRUE/FALSE) for compounds reduc-tion/selection. The retained number (304) of fluoroquinolone analogs (with TRUE outcome) defines the drug-like che-mical space (CombiData). Quantitative structure-activity relationships (QSAR) study on these 304 virtually generated 6-fluoroquinolone analogs with unknown activity values was performed using a pre-built five-parameter multiple linear regression (MLR) model developed on a set of compounds with experimentally determined activity values (R tr = 0.8417, R tr-cv = 0.7884). The obtained activity values for the unknown compounds together with the model results were used to define the applicability domain (AD). The obtained AD offers a good graphical representation and establishment of structure-activity relationships (SAR) which could be used for design of new 6-fluoroquinolones with possible better activity.</p

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment

<p>The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.</p

Quantitative structure-activity relationship study of antitubercular fluoroquinolones

<p>Quantitative structure-activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (μg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure-activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.</p

Investigation of 6-fluoroquinolones activity against Mycobacterium tuberculosis using theoretical molecular descriptors: a case study

<p>A quantitative structure-activity relationship (QSAR) study on a set of 66 structurally-similar 6-fluoroquinolones was performed using a large pool of theoretical molecular descriptors. Ab initio geometry optimizations were carried out to reproduce the geometrical and electronic structure parameters. The resulting molecular structures were confirmed to be minima via harmonic frequency calculations. Obtained atomic charges, HOMO and LUMO energies, orbital electron densities, dipole moment, energy and many other properties served as quantum-chemical descriptors. A multiple linear regression (MLR) technique was applied to generate a linear model for predicting the biological activity, Minimal Inhibitory Concentration (MIC), treated as negative decade logarithm, (pMIC). The heuristic method was used to optimize the model parameters and select the most significant descriptors. The model was tested internally using the CV LOO procedure on the training set and validated against the external validation set. The result (Q 2 ext = 0.7393), which was obtained on an external, previously excluded validation data set, shows the predictive performances of this model (R 2 tr = 0.7416, Q 2 tr = 0.6613) in establishing (Q)SAR of 6-fluoroquinolones. This validated model could be proficiently used to design new 6-fluoroquinolones with possible higher activity.</p

Study of the selectivity of alpha(1)-adrenergic antagonists by molecular modeling of alpha(1a)-, alpha(1b)-, and alpha(1d)-adrenergic receptor subtypes and docking simulations

Modeling of alpha(1a), alpha(1b), and alpha(1d) adrenergic receptor subtypes has been performed using InsightII software and bovine rhodopsin as a template. Adrenaline and noradrenaline, as endogenous agonists, were docked to validate the developed models, explore the putative binding sites, and calculate relative docking scores. alpha(1)-Adrenergic antagonists with the highest order of selectivity and activity at specific receptor subtypes were then chosen for docking into the corresponding receptor models. Docking simulations were performed using the FlexX module implemented in the Sybil program. PMF scoring functions of the obtained complexes calculated as relative to PMF scoring functions for noradrenaline-receptor subtype complexes were then used for correlation with selectivity on different alpha(1)-adrenergic subtypes. Good correlations were obtained for most receptor subtype-selectivity pairs: (1) using PMF scores calculated for ligands in complex with alpha(1a)-receptor subtype, r = 0.7503 for alpha(1a/1b) and r = 0.6336 for alpha(1a/1d) selectivity; (2) using PMF scores calculated for ligands in complex with alpha(1b) receptor subtype, r = 0.7632 for alpha(1a/1b) and r = 0.7061 for alpha(1b/1d) selectivity; (3) using PMF scores for ligands in complex with alpha(1d) receptor subtype, r = 0.7377 for alpha(1a/1d) and r = 0.9913 for alpha(1b/1d) selectivity.Peer reviewe