Abstract
The principal aim of our research was to develop theoretical models for estimating stability constants of coordination compounds, focusing on chelates of heavy metals, Cu(II) and Ni(II), with bioligands (amines, amino acids and peptides). We tried to solve the problem using graph-theoretical models. We also encountered a specific problem for coordination compounds--the choice of the constitutional formula, that is graph, of the complex species.
Highlights
The principal aim of our research was to develop theoretical models for estimating stability constants of coordination compounds, focusing on chelates of heavy metals, Cu(II) and Ni(II), with bioligands
The fundamental presumption in chemistry is that all properties of a matter correlate with its molecular structure, and that, molecules of similar structures have similar properties. This has led to a number of heuristic methods (QSPR, QSAR, QSTR, SAR, and SPC)
The reason may be that topological analysis traditionally belongs to organic chemistry or that coordination chemists have always been skeptical about molecular graph theory, which seems to be “too primitive” to cope with such a complex problem
Summary
The principal aim of our research was to develop theoretical models for estimating stability constants of coordination compounds, focusing on chelates of heavy metals, Cu(II) and Ni(II), with bioligands (amines, amino acids and peptides). The use of topological indices in QSPR analysis of organic compounds follows these steps: 1) construct molecular graphs, 2) correlate indices derived from graphs with experimental values, 3) choose the best indices, and 4) try to improve the results doing multiple regression by combining the indices. This simple scheme hardly works for coordination compounds because it is not at first clear which molecular species should be used as a basis for the graph. The researcher has to bear in mind that the graph always has to truthfully reflect real interactions in a molecule, and not serve as a mere tool for improving statistical parameters in a regression
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