Macrocyclic Receptors for Identification and Selective Binding of Substrates of Different Nature

Tetrahomo corona[4]arene-based spirophanes: synthesis, structure, and properties

In contrast to a plethora of macrocyclic and cage compounds, spirophanes have remained largely unexplored. We report herein the construction, structure and properties of unprecedented tetrahomo corona[4]arene-based ditopic and tritopic macrocycles of spiro structures. Synthesis was conveniently achieved by means of an efficient SNAr reaction from simple and commercially available starting materials. Racemic samples were resolved into enantiopure chiral tetrahomo i-corona[4]arenes, spirophanes and bispirophanes which show interesting chiroptical properties. The acquired electron-deficient macrocyclic compounds were found to adopt unique conformational structures and to form distinct complexes with TTF in the solid state. Our study provides a new opportunity to develop multitopic macrocycles of different topologies which have potential applications in supramolecular chemistry.

Molecular Recognition of Imidazole-Based Drug Molecules by Cobalt(III)- and Zinc(II)-Coproporphyrins in Aqueous Media

The methods of ¹H NMR, spectrophotometric titration, mass spectrometry and elemental analysis are applied to determine the selective binding ability of Co(III)- and Zn(II)-coproporphyrins I towards a series of imidazole-based drug molecules with a wide spectrum of pharmacological activity (metronidazole, histamine, histidine, tinidazole, mercazolil, and pilocarpine) in phosphate buffer (pH 7.4) simulating the blood plasma environment. It is shown that in aqueous buffer media, Co(III)-coproporphyrin I, unlike Zn(II)-coproporphyrin I, binds two imidazole derivatives, and the stability of mono-axial Co-coproporphyrin imidazole complexes is two to three orders of magnitude higher than that of similar complexes of Zn-coproporphyrin I. The studied porphyrinates are found to have the highest binding ability to histamine and histidine due to the formation of two additional hydrogen bonds between the carboxyl groups of the porphyrinate side chains and the binding sites of the ligands in the case of histidine and a hydrogen bond between the amino group of the ligand and the carbonyl oxygen atom of the carboxyl group of the porphyrinate in the case of histamine. The structures of the resulting complexes are optimized by DFT quantum chemical calculations. The results of these studies may be of use in the design of biosensors, including those for the detection, control and verification of various veterinary drug residues in human food samples.

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.

More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins

The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available.