The Compressed Feature Matrix - a novel descriptor for adaptive similarity search

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.