Substructural fragments are proposed as a simple and safe way to encode molecular structures in a matrix containing the occurrence of fragments of a given type. The knowledge retrieved from QSPR modelling can also be stored in that matrix in addition to the information about fragments. Complex supramolecular systems (using special bond types) and chemical reactions (represented as Condensed Graphs of Reactions, CGR) can be treated similarly. The efficiency of fragments as descriptors has been demonstrated in QSPR studies of aqueous solubility for a diverse set of organic compounds as well as in the analysis of thermodynamic parameters for hydrogen-bonding in some supramolecular complexes. It has also been shown that CGR may be an interesting opportunity to perform similarity searches for chemical reactions. The relationship between the density of information in descriptors/knowledge matrices and the robustness of QSPR models is discussed.

No abstract

A substructural molecular fragment (SMF) method has been developed to model the relationships between the structure of organic molecules and their thermodynamic parameters of complexation or extraction. The method is based on the splitting of a molecule into fragments, and on calculations of their contributions to a given property. It uses two types of fragments: atom/bond sequences and "augmented atoms" (atoms with their nearest neighbors). The SMF approach is tested on physical properties of C2-C9 alkanes (boiling point, molar volume, molar refraction, heat of vaporization, surface tension, melting point, critical temperature, and critical pressures) and on octanol/water partition coefficients. Then, it is applied to the assessment of (i) complexation stability constants of alkali cations with crown ethers and phosphoryl-containing podands, and of beta-cyclodextrins with mono- and 1,4-disubstituted benzenes, and (ii) solvent extraction constants for the complexes of uranyl cation by phosphoryl-containing ligands.

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). Comparison of the models was performed using a 5-fold external cross-validation procedure. Robust statistical tests (bootstrap and Kolmogorov-Smirnov statistics) were employed to evaluate the significance of calculated models. The Wilcoxon signed-rank test was used to compare the performance of methods. Individual structure-complexation property models obtained with nonlinear methods demonstrated a significantly better performance than the models built using multilinear regression analysis (MLRA). However, the averaging of several MLRA models based on SMF descriptors provided as good of a prediction as the most efficient nonlinear techniques. Support Vector Machines and Associative Neural Networks contributed in the largest number of significant models. Models based on fragments (SMF descriptors and E-state counts) had higher prediction ability than those based on E-state indices. The use of SMF descriptors and E-state counts provided similar results, whereas E-state indices lead to less significant models. The current study illustrates the difficulties of quantitative comparison of different methods: conclusions based only on one data set without appropriate statistical tests could be wrong.

Thermodynamics of 18‐crown‐6 complexes with ammonium cations (NH4, MeNH3, Me2NH2, Me3NH, Me4N, Et4N, PhNH3, and PhCH2NH3 ) in methanol were determined by titration calorimetry. The results show strong contributions from entropy terms counteracting the enthalpy of complexation, and a linear decrease of the complexation free energy ΔG with the number of available N–H hydrogen bonds. In several cases formation of relatively strong complexes containing two ammonium ions per crown unit was observed. Tetramethylammonium ions show no detectable association with the crown ether, demonstrating the absence of significant Coulomb‐type interaction between the partial charges at the crown ether oxygen and the N+–C–H atoms. Ammonium ions bind to aza crown ethers with almost equal affinity as to the all‐oxygen anologs only, if methyl groups at the nitrogen atoms force the lone pairs into equatorial position. Molecular mechanics calculations (CHARMm) of corresponding gas‐phase complexes yield geometries and energies in agreement with this, with energetically equally good conformations of an essentially undistorted D3d crown accepting either 3 linear hydrogen bonds, or 6 bifurcated bonds from the primary ammonium cations. Complexation equilibria were measured with PhNH3, and PhCH2NH3 in water, 2‐propanol, tert‐butyl alcohol, n‐octanol, DMF, DMSO, pyridine, HMPT and acetone mostly by calorimetry, in some cases by potentiometry. The observed association constants varied by factors of up to 1000; the solvent effects can be described generally as a linear function of the hydrogen bond accepting power of the solvent molecules, in line with the mechanisms derived above. The lgK and ΔH values of the complexation of the PhNH3 or PHCH2NH3 cation with 18‐crown‐6 ligand are compared with a large range of available solvent properties. The best correlations (R ≈ 0.9) for lgK (or ΔG) are obtained with values characterizing the electron donor capacity of the solvent (Ca, β*, DN) for lgK, as found earlier for complexes between K+ and 18C6.