Computer-aided design of new metal binders

Abstract

Chemoinformatics / In silico design / Complexation / ExtractionSummary. Chemoinformatics approaches open new opportunities for computer-aided design of new efficient metal binders. Here, we demonstrate performances of ISIDA and COMET software tools to predict stability constants (log K ) of the metal ion/organic ligand complexes in solution and to design in silico new molecules possessing desirable properties. The predictive models for log K of lanthanides complexation in water have been developed. Some new uranyl binders based on monoamides and on phosphoryl-containing podands were suggested theoretically, then synthesized and tested experimentally. Reasonable agreement between experimental uranyl distribution coefficients and theoretically predicted values has been observed.

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

“…). "Quorum Control" guided QSPR models perform better than previously reported models [23,39,40,52] for all studied metal ions. In whole, RMSE values of predictions obtained in this work are twice lower than those for the earlier reported models [23,39,40,52] (Figure 4) and they are close to experimental systematic errors (see Table 1).…”

“…[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.…”

“…[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…”

Scaffold Design Using Computational Chemistry

“…The resulting structure may reveal the extent to which the distance and geometry of binding interactions match their ideal values and the presence of other elements of strain that may impede formation of the host-guest complex. Computational methods have been extensively demonstrated for predicting the affinity and selectivity of multidentate ligands for metal ions [1][2][3][4][5]. A great deal of attention has also been given to the prediction of binding of small-molecule ligands to proteins for the purpose of docking and virtual drug screening, and similar methods may be applied to small-molecule receptors [6].…”