The compressed feature matrix—a fast method for feature based substructure search

Abstract

The compressed feature matrix (CFM) is a feature based molecular descriptor for the fast processing of pharmacochemical applications such as adaptive similarity search, pharmacophore development and substructure search. Depending on the particular purpose, the descriptor may be generated upon either topological or Euclidean molecular data. To assure a variable utilizability, the assignment of the structural patterns to feature types is arbitrarily determined by the user. This step is based on a graph algorithm for substructure search, which resembles the common substructure descriptors. While these merely allow a screening for the predefined patterns, the CFM permits a real substructure/subgraph search, presuming that all desired elements of the query substructure are described by the selected feature set. In this work, the CFM based substructure search is evaluated with regard to both the different outputs resulting from varying feature sets and the search speed. As a benchmark we use the programmable atom typer (PATTY) graph algorithm. When comparing the two methods, the CFM based matrix algorithm is up to several hundred times faster than PATTY and when using the CFM as a basis for substructure screening, the search speed is accelerated by three orders of magnitude. Thus, the CFM based substructure search complies with the requirements for interactive usage, even for the evaluation of several hundred thousand compounds. The concept of the CFM is implemented in the software COFEA.

Figure CFM based substructure search using the compounds dopamine and benzene-1,2-diol