New Approach for Accurate QSPR Modeling of Metal Complexation: Application to Stability Constants of Complexes of Lanthanide Ions Ln3+, Ag+, Zn2+, Cd2+ and Hg2+ with Organic Ligands in Water

Abstract

In this paper, we propose a new definition of models applicability domain (AD) based on the selection of sufficient portion of individual QSPR models to be accepted for property prediction. Efficiency of this approach has been demonstrated in ensemble modeling of the stability constants logK of the 1:1 complexes of 17 lanthanide and transition metal ions (M) with various organic ligands (L) ) metal ions with sets of diverse organic molecules in aqueous solution at 298 K and an ionic strength 0.1 M. The models have been validated by external 5-fold crossvalidation procedure. The root mean squared error (RMSE) of predictions is similar to systematic errors in experimental data. This is twice smaller compared to earlier reported models for which "quorum control" AD has not been applied. Methods Data SetsThe experimental stability constant (logK) ) metal ions with diverse organic ligands in water were selected from the IUPAC Stability Constants Database (SC DB) (version 5.33, Academic Software) [15] at standard temperature 298 K and an ionic strength I = 0.1 M. Some logK values (around 15 %) were corrected to specified temperature and an ionic strength using the procedures included in SC DB.2D structures of ligands, names of metal ions and corresponding logK values resulted from searching in SC DB were converted into Structure -Data Files (SDF) served as an input in the MLR module of the ISIDA (In Silico Design and Data Analysis) /QSPR program.[53] The data manager EdiSDF [35,46,54] was used to prepare data sets containing finally from 52 (Hg

GTM‐Based QSAR Models and Their Applicability Domains

“…The SOM‐based models built according to Equation 3 perform less well than corresponding GTM‐based models (Figure 5). Notice that the GTM models for the Gd 3+ and Lu 3+ perform similarly to MLR consensus models reported in the literature 21…”

“…These datasets were chosen in order to test performance of GTM‐based approaches on different classes of molecules (drug‐like, metal binders, small molecules with variable solubility). The Gd 3+ and Lu 3+ datasets have already been used in consensus QSPR modelling,21 which allows us to compare performance of different types of models.…”

The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling

“…The predicted complexation constants are evaluated as an arithmetic mean of values obtained by numerous individual eMLR models excluding those leading to outlying values and being outside AD of individual models. Three types of AD definitions can be used simultaneously or separately: bounding box, fragment control [52] and ''quorum control'' [30]. ''Quorum control'' is a threshold for the number of accepted models: ''no consensus without quorum''.…”

Supramolecular complexes: Determination of stability constants on the basis of various experimental methods

“…The IUPAC Committee on Equilibrium Data regularly analyzes the reliability of the values of equilibrium constants, but the numer ous recommended values [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] cover only a negli gible fraction of the known data. Lately, the impor tance of reliable values of equilibrium constants has kept increasing due to the fact that they are required in modern methods of chemical informatics that predict the stability of complexes of diverse ligands [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. The quality of prediction using QSPR models relating the ligand structure to the stability of the complex largely depends on the errors in the values of stability complexes used for development of models [51][52][53][54][55][56][57]59].…”

“…Lately, the impor tance of reliable values of equilibrium constants has kept increasing due to the fact that they are required in modern methods of chemical informatics that predict the stability of complexes of diverse ligands [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. The quality of prediction using QSPR models relating the ligand structure to the stability of the complex largely depends on the errors in the values of stability complexes used for development of models [51][52][53][54][55][56][57]59]. Simultaneous application of several experimental techniques and computer software capable of calculat ing constants on the basis of a set of methods allows controlling the values of systematic errors in estimates of This review describes the methods of calculating stability constants for supramolecular complexes in solutions and additional values, such as enthalpy, the electrode potential, the extinction coefficient, chemi β log β log β. log cal shift, and electrical conductivity, respectively, in the methods of calorimetry; potentiometry; and UV, IR, and NMR spectroscopy and conductometry on the basis of physicochemical characteristics related to equilibrium concentrations of chemical forms in solu tions.…”