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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

Abstract: The Compressed Feature Matrix (CFM) is a new molecular descriptor for adaptive similarity searching. Depending on the requirements, it is based on a distance or geometry matrix. Thus, the CFM permits topological and three-dimensional comparisons of molecules. In contrast to the common distance matrix, the CFM is based on features instead of atoms. Each kind of these features may be weighted separately, depending on its (estimated) contribution to the biological effect of the molecule. In this work, we show that the CFM allows us to adapt similarity evaluations to particular ligands as well as to classification requirements. The CFM method is analyzed regarding correctness, adaptivity and speed. Applying the basic setting of feature weights, the similarity evaluations using the CFM on the one hand and the Tanimoto coefficient together with MACCS Keys on the other yield similar results. However, in contrast to the latter method, the CFM even permits us to focus on small parts of molecules to serve as a basis for similarity. Accordingly, we have achieved striking results not only by readjusting the feature weights with regard to the scaffold but also to the side chain of the respective target. The results of the latter run turned out to be rather independent of the molecular scaffold. Hence, the CFM is suitable not only for common similarity evaluation, but also for techniques such as lead or scaffold hopping. Figure Chemical structure, feature graph and topological CFM of serotonine MediaObjects/s00894-002-0110-0flba.gif