QSPR ensemble modelling of alkaline-earth metal complexation

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

“…[51] In our studies for alkaline-earth [33] and some heavy metal ions, [43] the root-mean squared error (RMSE) of predictions is comparable with experimental systematic errors. [15] For lanthanide ions, [39,52] Ag + , [40] Zn 2+ , Cd…”

“…Molecules were represented with implicit hydrogen atoms. Two classes of the SMF [70] [ 71] descriptors were generated: shortest topological paths with explicit representation of atoms and bonds, and terminal groups as shortest paths but defined by length and explicit identification of terminal atoms and bonds [23,30,33,43] (Figure 2). Single, double and triple bonds were considered different in acyclic and cyclic non-aromatic motifs.…”

“…[23,30,33,46,73] MLR is applied to build linear relationships between independent variables (SMF descriptors: X i , i =1, 2,…) and a dependent variable (here target property Y = logK): Y = c 0 + Σc i X i , where every descriptor value (SMF count x ij , j = 1, 2,…, n; here n is the number of ligands) is associated with observed property value (y j , j = 1, 2,…, n), c i is descriptor contribution, and c 0 is the independent term which is omitted in a part of models. The Singular Value Decomposition method is used to fit contributions c i and to minimize the sum of squared residuals which are squared differences between the property values calculated by the model (y j , calc ) and observed values (y j , exp ) in the training set.…”

“…[15][16][17][18][19][20][21] This opens an opportunity to develop quantitative structure -property relationships (QSPR) linking the stability constants with the structure of ligands which, in turn, can be used for computeraided design of new metal binders. [22,23] To date, QSPR modeling of stability constants of the metal -ligand complexation was performed for alkali, [24][25][26][27][28][29][30][31][32] alkaline-earth, [32][33][34][35][36][37][38] rare-earth [39][40][41][42] and transition metal [23,34,36,37,40,[43][44][45] ions. In many cases the practical application of the reported QSPR is complicated due to the lack of complete information about descriptors' calculations and details of machine-learning method implementation.…”

“…In many cases the practical application of the reported QSPR is complicated due to the lack of complete information about descriptors' calculations and details of machine-learning method implementation. In order to overcome this problem, we have developed the COMET (COmplexation of METals) software [39] which implements previously elaborated QSPR models of stability constants (logK) of the 1:1 (M:L) complexes of diverse organic ligands with alkaline-earth (Sr 2+ , [33,35] , and UO 2 2+ [43] ) in water at 298 K and an ionic strength 0.1 M. All these models are based on substructural molecular fragments (SMF) [23,30,33] representing a subtype of the ISIDA descriptors. [46] SMF are subgraphs of molecular graph [30,47] whereas fragment occurrences are descriptor values.…”