Abstract
Subgraph and supergraph search methods are promising techniques for the development of new drugs. For example, the chemical structure of favipiravir—an antiviral treatment for influenza—resembles the structure of some components of RNA. Represented as graphs, such compounds are similar to a subgraph of favipiravir. However, the existing supergraph search methods can only discover compounds that match exactly. We propose a novel problem, called similar supergraph search, and design an efficient algorithm to solve it. The problem is to identify all graphs in a database that are similar to any subgraph of a query graph, where similarity is defined as edit distance. Our algorithm represents the set of candidate subgraphs by a code tree, which it uses to efficiently compute edit distance. With a distance threshold of zero, our algorithm is equivalent to an existing efficient algorithm for exact supergraph search. Our experiments show that the computation time increased exponentially as the distance threshold increased, but increased sublinearly with the number of graphs in the database.
Highlights
The coronavirus disease (COVID-19) has been spreading widely since 2019
The method proposed in this paper is a general and flexible framework for searching for similar subgraphs of query graphs and is customizable for searching for the types of graphs desired by the user
We proposed a novel problem, which is called similar supergraph search, and designed an efficient algorithm to solve the problem
Summary
The coronavirus disease (COVID-19) has been spreading widely since 2019. In Japan, favipiravir (brand name: Avigan) has been examined as a promising antiviral medication against COVID-19. If ribosylated favipiravir is given as the query and the substructure α is the output as the search result, we may be able to discover that ribosylated favipiravir is anti-influenza Such a search engine for chemical compounds would be very useful and effective. Users can search for their desired type of similar graphs in a database containing graphs and this search can be realized by rewriting only one function without the need to modify our proposed algorithm. The method proposed in this paper is a general and flexible framework for searching for similar subgraphs of query graphs and is customizable for searching for the types of graphs desired by the user.
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