Superstructure searches

Asymmetric simhaiity measures allow fuzzy super- and substructure searching. A substructure search is defined as looking for structures containing the given query and a superstructure search is defined as looking for structures embedded in the given query. In both cases asymmetric local similarity is estimated. 

Rodgers, David and W. T. Wipke. Artificial Intelligence in Organic Synthesis. SST Starting Material Selection Strategies. An Application of Superstructure Search, J. Chem. Inf. Comput. Sci., 24, (1984), pp. 71-81. 

At the very bottom level of most structure handling algorithms two structures are compared atom by atom and bond by bond (ref. 1). However, the preprocessing steps, the I/O conditions, the constraints in the query or in the reference structures, and requirements for a match or failure differ considerably from application to application. The most frequent used structure manipulating procedures are substructure and superstructure searches. 

In the substructure search the query (input) is small compared to the reference structures. The goal is to find all structures from the reference file that contain the query as a substructure. In the superstructure search the investigated (input) structure is large compared to structures in the reference file which is usually much shorter than in the case of substructure search. The purpose of the superstructure search is to identify all structures (skeletons, substituents, parts) from the reference file that fit into the query structure (Fig. 4.4). 

Superstructure search consists of a number of substructure searches in each of which the reference file contains only one structure, namely, the query. The number of substructure searches is equal to the number of structures in the reference file of the superstructure search. 

Fig. 4.4 A substructure (left) and superstructure (right) search. In the substructure search the query is usually smaller compared with the structures in the reference file, while in the case of superstructure search the reference file is much shorter and containes only small fragments and/or skeletons.

SUMEX project, 13 supercomputers, 177, 191 superstructure search, 75, 76 SuperText, 1 syntax error, 67 synthesis design, 135... 

Organic Synthesis. SST Starting Material Selection Strategies. An Application of Superstructure Search. 

Figure 6.14. Compounds identified from the ACD database through superstructure search.

Superstructure Search. Modification of substructure search in which the substructure query becomes the target structure, and the target structure in the database becomes the substructure search query. The search finds structures in the database that are substructures of the query. A similar extension to structure similarity searching yields superstructure similarity searching. 

A 2D similarity search is first carried out to find specific fragments in the database (e.g., CC(C==0)N for an amino acid). With the defined pharmacophore patterns, we will search the database using sub-/superstructure search, similarity search, Tanimoto coefficient (146), Tversky search (147), and so on. The search returned hit list will be grouped into a molecular spreadsheet for further studies. The 2D database searches usually are carried out as initial screening, followed by 3D searches. 

Recently, Wipke and Rogers described superstructure searching, which retrieves those molecules that are contained within the query structure, rather than the inverse. The same bit maps are used the difference is that for a substructure search the bit screen of the hit must contain all of the query bits, whereas for a superstructure search the query must contain all of the bits present in the hit. This type of search has applications in computer-aided design of synthetic pathways and in building three-dimensional structures from three-dimensional fragments. ... 

Figure 5 Compounds identified from a representative search for a structure that contains the query as a substructure (a substruaure search), or has a substructure of the query (a superstructure search), or is similar to the query (a similarity search).

By definition, any target structure that is a substructure (or superstructure) of the database molecule will have a subsimilarity (or supersimilarity) of 1.0, and hence a subsimilarity (supersimilarity) search can be viewed as a form of fuzzy substructure (superstructure) search. A further difference from the systems described thus far is in the nature of the output. The description of similarity searching that has been given previously assumes that a user browses down a ranked output until sufficient database structures have been identified. Here, conversely, the user specifies a threshold similarity value, and the output consists of an unordered list of all database structures (or database reactions) that have a similarity to the target that exceeds the chosen threshold value. 

By definition, any query which is a superstructure of the candidate, will give a similarity value of 100. Therefore, a super-similarity search can be viewed as a fuzzy superstructure search. 

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