In a database of about 2000 approved drugs, represented by 105 structural conformers,
we have performed 2D comparisons (Tanimoto coefficients) and 3D superpositions. For one class of drugs the correlation
between structural resemblance and similar action was analysed in detail.
In general Tanimoto cofficients and 3D scores give similar results, but we
find that 2D similarity measures neglect important structural/funtional
features. Examples for both over- and underestimation of similarity by
2D metrics are discussed. The required additional effort for 3D superpositions
is assessed by implementation of a fast algorithm with a processing
time below 0:01 seconds and a more sophisticated approach (0:5 seconds
per superposition). According to the improvement of similarity detection
compared to 2D screening and the pleasant rapidity on a desktop PC,
full{atom 3D superposition will be an upcoming method of choice for
library prioritization or similarity screening approaches.
Motivation: The Dictionary of Interfaces in Proteins (DIP) is a database collecting the 3D structure of interacting parts of proteins that are called patches. It serves as a repository, in which patches similar to given query patches can be found. The computation of the similarity of two patches is time consuming and traversing the entire DIP requires some hours. In this work we address the question of how the patches similar to a given query can be identified by scanning only a small part of DIP. The answer to this question requires the investigation of the distribution of the similarity of patches.
Results: The score values describing the similarity of two patches can roughly be divided into three ranges that correspond to different levels of spatial similarity. Interestingly, the two iso-score lines separating the three classes can be determined by two different approaches. Applying a concept of the theory of random graphs reveals significant structural properties of the data in DIP. These can be used to accelerate scanning the DIP for patches similar to a given query. Searches for very similar patches could be accelerated by a factor of more than 25. Patches with a medium similarity could be found 10 times faster than by brute-force search.