Effect of the stability of 1,3-bis(diphenylphosphoryl)-2-oxapropane complexes on the separation of lanthanide ions and their detection.

Phosphoryl podands of neutral type with a flexible ethylene glycol chain and diphenylphosphorylmethyl end groups are known for their complexation properties towards various cations. In this work, the complexation process between 1,3-bis(diphenylphosphoryl)-2-oxapropane (L) and lanthanide ions was studied. Namely, the stability constants of lanthanide complexes with L in acetonitrile were estimated by the method of spectrophotometric titration.

Synthesis, Complexation Properties, and Evaluation of New Aminodiphosphonic Acids as Vector Molecules for Ga Radiopharmaceuticals.

Two new aminodiphosphonic acids derived from salicylic acid and its phosphonic analogue were prepared through a simple and efficient synthesis. 2-[(2-Amino-2,2-diphosphono)ethyloxy]-benzoic acid and 2-[(2-amino-2,2-diphosphono)ethyloxy]-5-ethyl-phenylphosphonic acid were evaluated for their applicability as Ga binding bone-seeking agents. Protonation constants of and and stability constants of the Ga complexes with and in water were determined.

The impact of alicyclic substituents on the extraction ability of new family of 1,10-phenanthroline-2,9-diamides.

Development of efficient extractants for the separation of actinides and lanthanides in the technologies of nuclear fuel cycle is one of the most urgent and complex tasks in modern nuclear energetics. New family of 4,7-dichloro-1,10-phenanthroline-2,9-dicarboxylic acid diamides based on cyclic amines was synthesized and shown to exhibit high selectivity in the La/Am pair separation (SF (Am/La ≈ 10)) and in the Am/Eu pair separation (SF (Am/Eu ≈ 12)). It was shown that pyrrolidine derived diamide is more efficient extractant for americium, curium and lanthanides from highly acidic HNO solution than its non-cyclic ,,','-tetraalkyl analogues.

Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling.

Here, we report two-class classification models for organic molecules ("ligands") able to bind various metal cations in water. The modeling was performed on 30 data sets, each corresponding to a particular metal, using the Naïve Bayes method and the ISIDA fragment descriptors. The ligands were classified on weak and strong binders according to threshold of the logarithm of the stability constant of the 1 : 1 (metal : ligand) complexes.

Predictive Models for HOMO and LUMO Energies of N-Donor Heterocycles as Ligands for Lanthanides Separation.

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.

Predictive cartography of metal binders using generative topographic mapping.

Generative topographic mapping (GTM) approach is used to visualize the chemical space of organic molecules (L) with respect to binding a wide range of 41 different metal cations (M) and also to build predictive models for stability constants (logK) of 1:1 (M:L) complexes using "density maps," "activity landscapes," and "selectivity landscapes" techniques. A two-dimensional map describing the entire set of 2962 metal binders reveals the selectivity and promiscuity zones with respect to individual metals or groups of metals with similar chemical properties (lanthanides, transition metals, etc). The GTM-based global (for entire set) and local (for selected subsets) models demonstrate a good predictive performance in the cross-validation procedure.

Predictive Models for the Free Energy of Hydrogen Bonded Complexes with Single and Cooperative Hydrogen Bonds.

In this work, we report QSPR modeling of the free energy ΔG of 1 : 1 hydrogen bond complexes of different H-bond acceptors and donors. The modeling was performed on a large and structurally diverse set of 3373 complexes featuring a single hydrogen bond, for which ΔG was measured at 298 K in CCl . The models were prepared using Support Vector Machine and Multiple Linear Regression, with ISIDA fragment descriptors.

Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules.

Halogen bonding (XB) strength assesses the ability of an electron-enriched group to be involved in complexes with polarizable electrophilic halogenated or diatomic halogen molecules. Here, we report QSPR models of XB of particular relevance for an efficient screening of large sets of compounds. The basicity is described by pKBI2 , the decimal logarithm of the experimental 1 : 1 (B : I2 ) complexation constant K of organic compounds (B) with diiodine (I2 ) as a reference halogen-bond donor in alkanes at 298 K.

Individual Hydrogen-Bond Strength QSPR Modelling with ISIDA Local Descriptors: a Step Towards Polyfunctional Molecules.

Here, we introduce new ISIDA fragment descriptors able to describe "local" properties related to selected atoms or molecular fragments. These descriptors have been applied for QSPR modelling of the H-bond basicity scale pKBHX , measured by the 1 : 1 complexation constant of a series of organic acceptors (H-bond bases) with 4-fluorophenol as the reference H-bond donor in CCl4 at 298 K. Unlike previous QSPR studies of H-bond complexation, the models based on these new descriptors are able to predict the H-bond basicity of different acceptor centres on the same polyfunctional molecule.

QSPR ensemble modelling of the 1:1 and 1:2 complexation of Co²⁺, Ni²⁺, and Cu²⁺ with organic ligands: relationships between stability constants.

Quantitative structure-property relationship (QSPR) modeling of stability constants for the metal:ligand ratio 1:1 (logK) and 1:2 (logβ2) complexes of 3 transition metal ions with diverse organic ligands in aqueous solution was performed using ensemble multiple linear regression analysis and substructural molecular fragment descriptors. The modeling was performed on the sets containing 396 and 132 (Co(2+)), 613 and 233 (Ni(2+)), 883 and 257 (Cu(2+)) logK and logβ 2 values, respectively. The models have been validated in external fivefold cross-validations procedure as well as on the external test set containing new ligands recently reported in the literature.

Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches.

This work addresses the problem of similarity search and classification of chemical reactions using Neighborhood Behavior (NB) and Condensed Graphs of Reaction (CGR) approaches. The CGR formalism represents chemical reactions as a classical molecular graph with dynamic bonds, enabling descriptor calculations on this graph. Different types of the ISIDA fragment descriptors generated for CGRs in combination with two metrics--Tanimoto and Euclidean--were considered as chemical spaces, to serve for reaction dissimilarity scoring.

Determination of successive complexation constants in an ionic liquid: complexation of UO(2)(2+) with NO(3)(-) in C(4)-mimTf(2)N studied by UV-Vis spectroscopy.

The complexation of UO(2)(2+) with NO(3)(-) has been investigated in the ionic liquid 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide by UV-vis spectroscopy at T = 18.5 degrees C. The complexation is evidenced through the appearance of four peaks at 425, 438, 453, and 467 nm.

Stochastic versus stepwise strategies for quantitative structure-activity relationship generation--how much effort may the mining for successful QSAR models take?

Descriptor selection in QSAR typically relies on a set of upfront working hypotheses in order to boil down the initial descriptor set to a tractable size. Stepwise regression, computationally cheap and therefore widely used in spite of its potential caveats, is most aggressive in reducing the effectively explored problem space by adopting a greedy variable pick strategy. This work explores an antipodal approach, incarnated by an original Genetic Algorithm (GA)-based Stochastic QSAR Sampler (SQS) that favors unbiased model search over computational cost.

Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?

Several popular machine learning methods--Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), modified version of the partial least-squares analysis (PLSM), backpropagation neural network (BPNN), and Multiple Linear Regression Analysis (MLR)--implemented in ISIDA, NASAWIN, and VCCLAB software have been used to perform QSPR modeling of melting point of structurally diverse data set of 717 bromides of nitrogen-containing organic cations (FULL) including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT). Several types of descriptors were tested: E-state indices, counts of atoms determined for E-state atom types, molecular descriptors generated by the DRAGON program, and different types of substructural molecular fragments. Predictive ability of the models was analyzed using a 5-fold external cross-validation procedure in which every compound in the parent set was included in one of five test sets.

Skin permeation rate as a function of chemical structure.

Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log K(p)) of a set of 143 diverse compounds. Satisfactory models were obtained by three approaches applied: (i) CODESSA PRO, (ii) Neural Network modeling using large pools of theoretical molecular descriptors, and (iii) ISIDA modeling based on fragment descriptors. The predictive abilities of the models were assessed by internal and external validations.

Correlation of blood-brain penetration using structural descriptors.

Experimental blood-brain partition coefficients (logBB) for a diverse set of 113 drug molecules are correlated with computed structural descriptors using CODESSA-PRO and ISIDA programs to give statistically significant QSAR models based respectively, on molecular and on fragment descriptors. The linear correlation CODESSA-PRO five-descriptor model has correlation coefficient R2=0.781 and standard deviation s2=0.

Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.

A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 complexes of metal cations Ag+ and Eu3+ with diverse sets of organic molecules in water at 298 K and ionic strength 0.1 M. The methods were tested on three types of descriptors: molecular descriptors including E-state values, counts of atoms determined for E-state atom types, and substructural molecular fragments (SMF).

QSAR modeling of blood:air and tissue:air partition coefficients using theoretical descriptors.

Human blood:air, human and rat tissue (fat, brain, liver, muscle, and kidney):air partition coefficients of a diverse set of organic compounds were correlated and predicted using structural descriptors by employing CODESSA-PRO and ISIDA programs. Four and five descriptor regression models developed using CODESSA-PRO were validated on three different test sets. Overall, these models have reasonable values of correlation coefficients (R(2)) and leave-one-out correlation coefficients (R(cv)(2)): R(2) = 0.

Quantitative structure-property relationship modeling of beta-cyclodextrin complexation free energies.

CODESSA-PRO was used to model binding energies for 1:1 complexation systems between 218 organic guest molecules and beta-cyclodextrin, using a seven-parameter equation with R2 = 0.796 and Rcv2 = 0.779.