Quantitative structure–activity relationship studies of a series of non-benzodiazepine structural ligands binding to benzodiazepine receptor

Synthesis of benzofuro- and benzothieno[2,3-c]pyridines via copper-catalyzed [4 + 2] annulation of ketoxime acetates with acetoacetanilide

An efficient copper-catalyzed annulation of ketoxime acetates with acetoacetanilide has been developed for the facile synthesis of benzofuro- and benzothieno[2,3-c]pyridines.

The synthesis of fluorescent benzofuro[2,3-c]pyridines via palladium-catalyzed heteroaromatic C–H addition and sequential tandem cyclization

A Pd-catalyzed tandem reaction of 2-(cyanomethoxy)chalcones with heteroarenes (such as thiophenes, furans, pyrroles and indoles) for the synthesis of fluorescent benzofuro[2,3-c]pyridines.

Palladium meets copper: one-pot tandem synthesis of pyrido fused heterocycles via Sonogashira conjoined electrophilic cyclization

An efficient step-economical tandem approach for the direct synthesis of pyrido fused indole, quinoline, benzofuran and benzothiophene derivatives using a bimetallic Pd/Cu catalytic system has been described. The three component reaction of o-halo aldehydes, alkynes and tert-butylamine leads to the synthesis of biologically active polyheterocycles. The present strategy involves the dual role of tert-butylamine and copper(i) in Sonogashira coupling followed by electrophilic cyclization through imine formation. The chemistry has been validated by X-ray crystallographic studies.

Xanthenones: calixarenes-catalyzed syntheses, anticancer activity and QSAR studies

An efficient method is proposed for obtaining tetrahydrobenzo[a]xanthene-11-ones and tetrahydro-[1,3]-dioxolo[4,5-b]xanthen-9-ones.

Synthesis of benzofuro[2,3-c]pyridines via a one-pot three-component reaction

A convenient one-pot synthesis of polysubstituted benzofuro[2,3-c]pyridines by three-component is developed. It provides a flexible and rapid synthetic route for the construction of benzofuro[2,3-c]pyridines under mild and metal-free reaction conditions in moderate to good yields (up to 83%).

Indolo[3,2-a]carbazoles from a deep-water sponge of the genus Asteropus

Two new indolo[3,2-a]carbazoles (1, 2) were isolated from a deep-water collection of a sponge of the genus Asteropus. The structures of 1 and 2 were determined through the analysis of spectroscopic data including mass spectrometry and 2D-NMR. Compound 1 showed minimum inhibitory concentrations of 25 μg/mL against the fungal pathogen Candida albicans and 50 μg/mL against methicillin-resistant Staphylococcus aureus (MRSA). Compounds 1 and 2 showed no cytotoxicity against the PANC1 human pancreatic carcinoma and NCI/ADR-RES ovarian adenocarcinoma cell lines at our standard test concentration of 5 μg/mL.

Synthesis of rubrolide analogues as new inhibitors of the photosynthetic electron transport chain

Many natural products have been used as a model for the development of new drugs and agrochemicals. Following this strategy 11 rubrolide analogues, bearing electron-withdrawing and -donating groups at both benzene rings, were prepared starting from commercially available mucobromic acid. The ability of all compounds to inhibit the photosynthetic electron transport chain in the chloroplast was investigated. The rubrolide analogues were effective in interfering with the light-driven ferricyanide reduction by isolated chloroplasts. The IC(50) values of the most active derivatives are in fact only 1 order of magnitude higher than those of commercial herbicides sharing the same mode of action, such as Diuron (0.27 μM). QSAR studies indicate that the most efficient compounds are those having higher ability to accept electrons, either by a reduction process or by an electrophilic reaction mechanism. The results obtained suggest that the rubrolide analogues represent promising candidates for the development of new active principles targeting photosynthesis to be used as herbicides.

Prediction of the maximum absorption wavelength of azobenzene dyes by QSPR tools

The maximum absorption wavelength (λ(max)) of a large data set of 191 azobenzene dyes was predicted by quantitative structure-property relationship (QSPR) tools. The λ(max) was correlated with the 4 molecular descriptors calculated from the structure of the dyes alone. The multiple linear regression method (MLR) and the non-linear radial basis function neural network (RBFNN) method were applied to develop the models. The statistical parameters provided by the MLR model were R(2)=0.893, R(adj)(2)=0.893, q(LOO)(2)=0.884, F=1214.871, RMS=11.6430 for the training set; and R(2)=0.849, R(adj)(2)=0.845, q(ext)(2)=0.846, F=207.812, RMS=14.0919 for the external test set. The RBFNN model gave even improved statistical results: R(2)=0.920, R(adj)(2)=0.919, q(LOO)(2)=0.898, F=1664.074, RMS=9.9215 for the training set, and R(2)=0.895, R(adj)(2)=0.892, q(ext)(2)=0.895, F=314.256, RMS=11.6427 for the external test set. This theoretical method provides a simple, precise and an alternative method to obtain λ(max) of azobenzene dyes.

QSAR modeling of anxiolytic activity taking into account the presence of keto- and enol-tautomers by balance of correlations with ideal slopes

Optimal descriptors calculated with simplified molecular input line entry system (SMILES) have been examined as a tool for prediction of anxiolytic activity. Descriptors calculated with SMILES (a) of keto-isomers; (b) of enol-isomers; and (c) of both keto-isomers together with enol-isomers have been studied. Three approaches have been compared: 1. classic’ training-test’ system 2. balance of correlations and 3. balance of correlations with ideal slopes. The best statistical characteristics for the external validation set took place for optimal descriptors calculated with SMILES of both keto-form and enol-form (i.e., molecular structure was represented in the format: ’sMILES of keto-form. SMILES of enol-form’) by means of balance of correlations with ideal slopes. The predictive potential of this model was checked with three random splits.

Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks

In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.

Prediction of the retention of β-diketonato complexes in TLC systems on silica gel by quantitative structure-retention relationships

Quantitative structure-retention relationships for a series of 30 mixed ?-diketonato complexes of cobalt(III), chromium(III) and ruthenium(III) were derived by multiple linear regression analyses using molecular descriptors obtained by quantum chemical calculations. The retention parameters were obtained by thin layer chromatography on silica gel using mono and two-component solvent systems. The molecular descriptors included in the multiple linear regression analysis were molecular weight, molecular volume, surface area, hydrophilic-lipophilic balance, percent hydrophilic surface area, dipole moment, polarizability, refractivity, energy of the highest occupied molecular orbital and energy of the lowest unoccupied molecular orbital. High agreement between the experimental and predicted retention parameters was obtained when polarizability and the hydrophilic-lipophilic balance were used as the molecular descriptors. Comparison of the models with those established on polyacrylonitrile showed that the structure of the sorbent is responsible for the chromatographic behaviour of the same compounds. The presented models can be used for the prediction of the retention of new solutes in screening chromatographic systems.

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

The half-wave potential (E1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R2=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E1/2, and can be useful in predicting the E1/2 of other compounds.

Current mathematical methods used in QSAR/QSPR studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.