Cup-shaped E,E-stilbenophane: synthesis,crystal structure and supramolecular chemistry

A new E,E-stilbenophane was synthesised and characterised. The crystal structure of this cyclophane shows that this molecule has a cup-shaped structure, which hosts a phenyl ring of neighbouring molecule as guest in its cavity with a π–π distance of about 3.7 Å. Moreover, the NMR spectra and theoretical analysis (gauge-independent atomic orbitals (GIAO) and quantum theory of atoms in molecules (QTAIM)) suggest that the silver recognition by E,E-stilbenophane host molecules is based on cation–π interactions in which the π-electrons of the double bonds play a major role.     

Interaction between tetramethylcucurbit[6]uril with α-furaldehyde-isonicotinyl-hydrazone hydrochloride

Interaction between tetramethylcucurbit[6]uril (TMeQ[6], host) and the hydrochloride salt of α-furaldehyde-isonicotinyl-hydrazone hydrochloride (FIHH⁺, guest) was investigated using X-ray crystallography and spectroscopic methods. X-ray crystallography showed that the π–π stacking effect and hydrogen bonding resulted in the formation of a dumbbell-shaped supramolecule which contained two FIHH⁺@TMeQ[6] host–guest inclusion complexes. The host–guest interaction provided identifiable changes in the vibrational frequencies in the IR spectra. ¹H NMR spectral analysis established a similar interaction model and revealed that TMeQ[6] preferred to include the furan moiety over the pyridine moiety of the FIHH⁺ guest molecule. Absorption spectrophotometric analysis suggested that the host and guest interact in a ratio of 1:1 with a stability constant K _s = (3.52 ± 0.74) × 10⁶ l mol^? 1.pH titration confirmed that the host–guest interaction led to a clear change in the protonation constant of the title guest. Quantum chemical calculations were used to determine the possible mechanism of formation of the dumbbell-shaped complex.     

Chromogenic amides of pyridine-2,6-dicarboxylic acid as anion receptors

The synthesis of simple, chromogenic pyridine-2,6-dicarboxylic acid amides, derivatives of isomeric nitroanilines and aminonitrophenols, and their ion binding properties are described. The ligands' response to ionic species was examined by naked eye and was studied with the use of UV–vis spectroscopy in DMSO and its mixture with water. The effect of the localisation and the type of the substituents in aromatic rings were discussed. ¹H NMR experiments were carried out to probe the mechanism of anion recognition, i.e. complexation via hydrogen bond formation versus ligand deprotonation. A selective response of N,N′-bis(2-hydroxy-4-nitrophenyl)pyridine-2,6-dicarboxamide (L5) towards dihydrogen phosphate was found in both DMSO and DMSO–water (95:5) solvent mixture. The structure of N,N′-bis(2-hydroxy-5-nitrophenyl)pyridine-2,6-dicarboxamide (L4) was confirmed by X-ray crystallography.     

Evaluation of SARs for the prediction of skin irritation/corrosion potential–structural inclusion rules in the BfR decision support system†

The German Federal Institute for Risk Assessment (BfR) has developed a Decision Support System (DSS) to assess certain hazardous properties of pure chemicals, including skin and eye irritation/corrosion. The BfR–DSS is a rule-based system that could be used for the regulatory classification of chemicals in the European Union. The system is based on the combined use of two predictive approaches: exclusion rules based on physicochemical cut-off values to identify chemicals that do not exhibit a certain hazard (e.g., skin irritation/corrosion), and inclusion rules based on structural alerts to identify chemicals that do show a particular toxic potential. The aim of the present study was to evaluate the structural inclusion rules implemented in the BfR–DSS for the prediction of skin irritation and corrosion. The following assessments were performed: (a) a confirmation of the structural rules by rederiving them from the original training set (1358 substances), and (b) an external validation by using a test set of 200 chemicals not used in the derivation of the rules. It was found as a result that the test data set did not match the training set relative to the inclusion of structural alerts associated with skin irritation/corrosion, albeit some skin irritants were in the test set.     

Development of an information-intensive structure–activity relationship model and its application to human respiratory chemical sensitizers

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

Theory of docking scores and its application to a customizable scoring function

In general, the docking scoring tends to have a size dependence related to the ranking of compounds. In this paper, we describe a novel method of parameter optimization for docking scores which reduce the size dependence and can efficiently discriminate active compounds from chemical databases. This method is based on a simplified theoretical model of docking scores which enables us to utilize large amounts of data of known active and inactive compounds for a particular target without requiring large computational resources or a complicated procedure. This method is useful for making scoring functions for the identification of novel scaffolds using the knowledge of active compounds for a particular target or a customized scoring function for an interesting family of drug targets.