Similarity search approach

To become familiar with the basics of chemical structure similarity, similarity measures, and different approaches exploited within the similarity search process. 

Structure and substructure searching are very powerful ways of accessing a database, but they do assume that the searcher knows precisely the information that is needed, that is, a specific molecule or a specific class of molecules, respectively. The third approach to database searching, similarity searching, is less precise in nature because it searches the database for molecules that are similar to the user s query, without formally defining exactly how the molecules should be related (Fig. 8.3). 

The second postprocessing step is the automated enhancement of the TrEMBL annotation to bring TrEMBL entries closer to SWISS-PROT standard. There is an increasing need for reliable automatic functional annotation to cope with the rapidly increasing amount of sequence data. Most of the current approaches are still based on sequence similarity searches against known proteins. Some groups try to collect the results of different prediction tools in a simple way, e.g., PEDANT (Frishman and Mewes, 1997) or GeneQuiz (Scharf et al., 1994). However, several pitfalls of these methods have been reported (Bork and Koonin, 1998). 

How does one go about finding all of the relevant proteins in a database once it has been decided to carry out an analysis of an entire protein family The simplest approach is to use similarity search software such as SSEARCH or FASTA (Smith and Waterman, 1981 Pearson and Lipman, 1988) or BLAST (Altschul et al, 1997) with the amino acid sequences of one or two well-known members of the family as queries. The problem is initially the same as that of identifying all proteins that are homologous to a family of proteins, although with some important practical differ-... 

The standard screening approach when several active molecules have been identified is pharmacophore mapping followed by 3D database searching. This approach assumes that the active molecules have a common mode of action and that features that are common to all of the molecules describe the pharmacophoric pattern responsible for the observed bioactivity. This is a powerful technique but one that may not be applicable to the structurally heterogeneous hits that characterize typical HTS experiments or sets of competitor compounds drawn from the public literature. In such cases, it is appropriate to consider approaches based on 2D similarity searching and we present here a comparison of approaches for combining the structural information that can be gleaned from a small set of reference structures. 

The following summarizes three related computational approaches that enable the prediction of target or mechanism of action (MOA) from chemical stmcture chemical similarity searching, data mining/machine learning and bioactivity spectra. 

While we were aware of the latent potential of COSMO-RS for drug similarity searches, it was a customer, Michael Thormann from MORPHOCHEM, who did the first similarity searches based on COSMO-RS a-profiles. He tried the simplest approach of -profile comparison. Applying a generalization of Tanimoto s binary similarity coefficient for real numbers to (7-profiles, the defined the similarity of two molecules X and X as... 

Many strategies have been proposed to enrich a set of molecules with active compounds by virtual screening similarity search [49], docking-scoring [50], QSAR [51] and, of course, pharmacophore models. This section reviews some methods that can be applied to any of those approaches. 

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