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The fact of the fracture of the extraction curve of lanthanides by 1,10-phenanthroline-2,9-diamides is explained in terms of the structure of complexes, solvent extraction data and quantum chemical calculations. The solvent extraction proceeds in two competing directions: in the form of neutral complexes L Ln(NO 3 ) 3 and in the form of tight ion pairs {[ L Ln(NO 3 ) 2 H 2 O] + (NO 3 - ).

The analysis of information on the spatial structure of molecules and the physical fields of their interactions with biological targets is extremely important for solving various problems in drug discovery. This mini-review article surveys the main features of the continuous molecular fields approach and its use for analyzing structure-activity relationships in 3D space, building 3D quantitative structure-activity models and conducting similarity based virtual screening. Particular attention is paid to the consideration of the concept of molecular co-fields and their use for the interpretation of 3D structure-activity models. The principles of molecular design based on the overlapping and the similarity of molecular fields with corresponding co-fields are formulated.

Quantum chemical calculations combined with QSPR methodology reveal challenging perspectives for the solution of a number of fundamental and applied problems. In this work, we performed the PM7 and DFT calculations and QSPR modeling of HOMO and LUMO energies for polydentate N-heterocyclic ligands promising for the extraction separation of lanthanides because these values are related to the ligands selectivity in the respect to the target cations. Data for QSPR modeling comprised the PM7 calculated HOMO and LUMO energies of N-donor heterocycles, including several types of both known and virtual undescribed polydentate ligands. Ensemble modeling included various molecular fragments as descriptors and different variable selection techniques to build consensus models (CMs) on a training set of 388 ligands using external cross-validation. CMs were then verified to make predictions for two external test sets: 45 ligands (T1) that were similar to the ligands of the training set, and 1546 structures (T2), which were substantially different from the ligands of the training set. The consensus models predict well in 5-fold cross-validation (RMSE HOMO =0.097 eV, RMSE LUMO =0.064 eV), and on the external test sets (T1: RMSE HOMO =0.26 eV, RMSE LUMO =0.24 eV; T2: RMSE HOMO =0.26 eV, RMSE LUMO =0.17 eV). An analysis of the results reveals that substituents in heteroaromatic rings of the ligands and at the amide nitrogens can deeply influence their metal binding properties., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

In Energy-Based Neural Networks (EBNNs), relationships between variables are captured by means of a scalar function conventionally called "energy". In this article, we introduce a procedure of "harmony search", which looks for compounds providing the lowest energies for the EBNNs trained on active compounds. It can be considered as a special kind of similarity search that takes into account regularities in the structures of active compounds. In this paper, we show that harmony search can be used for performing virtual screening. The performance of the harmony search based on two types of EBNNs, the Hopfield Networks (HNs) and the Restricted Boltzmann Machines (RBMs), was compared with the performance of the similarity search based on Tanimoto coefficient with "data fusion". The AUC measure for ROC curves and 1 %-enrichment rates for 20 targets were used in the benchmarking. Five different scores were computed: the energy for HNs, the free energy and the reconstruction error for RBMs, the mean and the maximum values of Tanimoto coefficients. The performance of the harmony search was shown to be comparable or even superior (significantly for several targets) to the performance of the similarity search. Important advantages of using the harmony search for virtual screening are very high computational efficiency of prediction, the ability to reveal and take into account regularities in active structures, flexibility and interpretability of models, etc., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A novel type of molecular fields, Continuous Indicator Fields (CIFs), is suggested to provide 3D structural description of molecules. The values of CIFs are calculated as the degree to which a point with given 3D coordinates belongs to an atom of a certain type. They can be used similarly to standard physicochemical fields for building 3D structure-activity models. One can build CIF-based 3D structure-activity models in the framework of the continuous molecular fields approach described earlier (J Comput-Aided Mol Des 27 (5):427-442, 2013) for the case of physicochemical molecular fields. CIFs are thought to complement and further extend traditional physicochemical fields. The models built with CIFs can be interpreted in terms of preferable and undesirable positions of certain types of atoms in space. This helps to understand which changes in chemical structure should be made in order to design a compound possessing desirable properties. We have demonstrated that CIFs can be considered as 3D analogues of 2D topological molecular fragments. The performance of this approach is demonstrated in structure-activity studies of thrombin inhibitors, multidentate N-heterocyclic ligands for Am(3+)/Eu(3+) separation, and coloring dyes.

The continuous molecular fields (CMF) approach is based on the application of continuous functions for the description of molecular fields instead of finite sets of molecular descriptors (such as interaction energies computed at grid nodes) commonly used for this purpose. These functions can be encapsulated into kernels and combined with kernel-based machine learning algorithms to provide a variety of novel methods for building classification and regression structure-activity models, visualizing chemical datasets and conducting virtual screening. In this article, the CMF approach is applied to building 3D-QSAR models for 8 datasets through the use of five types of molecular fields (the electrostatic, steric, hydrophobic, hydrogen-bond acceptor and donor ones), the linear convolution molecular kernel with the contribution of each atom approximated with a single isotropic Gaussian function, and the kernel ridge regression data analysis technique. It is shown that the CMF approach even in this simplest form provides either comparable or enhanced predictive performance in comparison with state-of-the-art 3D-QSAR methods.

A solid-phase enzyme immunoassay procedure for detecting natural antibodies to vasopressin (VP) is developed. For this purpose, a VP antigen is synthesized on polymer matrix. Optimal conditions of enzyme immunoassay for detecting antibodies to this antigen in the sera of donors and patients with systemic lupus erythematosus (SLE) are selected. The level of anti-VP antibodies is constant in donors and shifted in SLE patients. Changes in the level of natural antibodies to VP correlate with immunochemical parameters.