Rationale and History

Nevertheless, several proposed approaches are based on the concept of a superstructure spanning the variability of molecules in a dataset. It can be thought of as a topological network that allows superimposing of every dataset structure. This brings their atoms and bonds into the same frame of reference, yielding the uniform representation of the local molecular properties. 

Notably, the concept of super- or hyperstructure is sometimes used in chemo-informatics as a way to build the most compact representation of a series of structures for easy database storage and retrieval. Some attempts were made to take qualitative activity data into account during the construction of a hyperstructure. However, the primary goal of compactness dictates certain hyperstructure features and algorithms that complicate its chemical interpretation and use in QSAR modeling. 

On the other hand, several superstructural approaches were designed specifically for the QSAR analysis and lead optimization for organic compounds. Let us consider them in more detail. 

The DARC/PELCO method (Methode de perturbation d environnements limites concentriques ordonnes) for predicting the properties of organic 

If necessary for the analysis, secondary descriptors may be included that are constructed as logical combinations of the primary descriptors. In further development of the method, it was proposed to include the so-called external physicochemical properties of a whole molecule, e.g., lipophilicity and quantum-chemical parameters important for the mechanism of action. Unfortunately, in recent years, the progress of this approach seems to have ceased. 

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