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Two new organo-inorganic hybrids, (C2N2H10)[Cu(H2O)4](BeF4)2 (1) and (C2N2H10)[Cu(H2O)4](SeO4)2 (2), were prepared via the interaction of ethylenediamine, copper fluoroberyllate or selenate, and H2[BeF4]/H2SeO4 in aqueous solutions. The structures of 1 and 2 are similar to each other and the previously reported (C2N2H10)[Cu(H2O)4](SO4)2: monoclinic, P21/c, a = 5.1044(2) Å, b = 11.6171(4) Å, c = 10.1178(3) Å, and β = 94.431(3)° for 1; and a = 5.25020(10), b = 11.7500(2), c = 10.4434(2), and β = 94.5464(17)° for 2. All structures contain a square planar [Cu(H2O)4]2+ species, which coordinates, at rather long distances, two TX42− tetrahedral dianions in κ1 mode, forming relatively weak [Cu(H2O)4(TX4)2]2− complexes. These are linked together via hydrogen bonding into pseudo-chains; the ethylenediammonium cations link them into a 3D architecture. Compound 1 is, to the best of our knowledge, the first—though expected—representative of a hybrid organo-inorganic fluoroberyllate. The crystal chemical relations within the structural family (enH2)[Cu(H2O)4](TX4)2 are discussed. [ABSTRACT FROM AUTHOR]

Two new organo-inorganic hybrids, (C2N2H10)[Cu(H2O)4](BeF4)2 (1) and (C2N2H10)[Cu(H2O)4](SeO4)2 (2), were prepared via the interaction of ethylenediamine, copper fluoroberyllate or selenate, and H2[BeF4]/H2SeO4 in aqueous solutions. The structures of 1 and 2 are similar to each other and the previously reported (C2N2H10)[Cu(H2O)4](SO4)2: monoclinic, P21/c, a = 5.1044(2) Å, b = 11.6171(4) Å, c = 10.1178(3) Å, and β = 94.431(3)° for 1; and a = 5.25020(10), b = 11.7500(2), c = 10.4434(2), and β = 94.5464(17)° for 2. All structures contain a square planar [Cu(H2O)4]2+ species, which coordinates, at rather long distances, two TX42− tetrahedral dianions in κ1 mode, forming relatively weak [Cu(H2O)4(TX4)2]2− complexes. These are linked together via hydrogen bonding into pseudo-chains; the ethylenediammonium cations link them into a 3D architecture. Compound 1 is, to the best of our knowledge, the first—though expected—representative of a hybrid organo-inorganic fluoroberyllate. The crystal chemical relations within the structural family (enH2)[Cu(H2O)4](TX4)2 are discussed.

Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For SAR analysis, 3D AL models are particularly intuitive. In these models, an interpolated potency surface is added as a third dimension to a 2D projection of chemical space. Accordingly, AL topology can be associated with characteristic SAR features. Going beyond visualization and a qualitative assessment of SARs, it would be very helpful to compare 3D ALs of different datasets in more quantitative terms. However, quantitative AL analysis is still in its infancy. Recently, it has been shown that 3D AL models with pre-defined topologies can be correctly classified using machine learning. Classification was facilitated on the basis of AL image feature representations learned with convolutional neural networks. Therefore, we have further investigated image analysis for quantitative comparison of 3D ALs and devised an approach to determine (dis)similarity relationships for ALs representing different compound datasets. Herein, we report this approach and demonstrate proof-of-principle. The methodology makes it possible to computationally compare 3D ALs and quantify topological differences reflecting varying SAR information content. For SAR exploration in drug design, this adds a quantitative measure of AL (dis)similarity to graphical analysis.

Activity landscape (AL) models are used for visualizing and interpreting structure–activity relationships (SARs) in compound datasets. Therefore, ALs are designed to present chemical similarity and compound potency information in context. Different two- or three-dimensional (2D or 3D) AL representations have been introduced. For SAR analysis, 3D AL models are particularly intuitive. In these models, an interpolated potency surface is added as a third dimension to a 2D projection of chemical space. Accordingly, AL topology can be associated with characteristic SAR features. Going beyond visualization and a qualitative assessment of SARs, it would be very helpful to compare 3D ALs of different datasets in more quantitative terms. However, quantitative AL analysis is still in its infancy. Recently, it has been shown that 3D AL models with pre-defined topologies can be correctly classified using machine learning. Classification was facilitated on the basis of AL image feature representations learned with convolutional neural networks. Therefore, we have further investigated image analysis for quantitative comparison of 3D ALs and devised an approach to determine (dis)similarity relationships for ALs representing different compound datasets. Herein, we report this approach and demonstrate proof-of-principle. The methodology makes it possible to computationally compare 3D ALs and quantify topological differences reflecting varying SAR information content. For SAR exploration in drug design, this adds a quantitative measure of AL (dis)similarity to graphical analysis. [ABSTRACT FROM AUTHOR]