Similarity Concepts and Database Mining
There are two pharmacophore similarity concepts in Moloc:
- 3-dimensional pharmacophores and full structures can be rigidly
superimposed by optimally matching the pharmacophore elements (agons).
The result is a similarity index with values between zero and one.
- Topological pharmacophores can be compared by matching like
agons in all possible ways and by selecting the optimal pairing
on criteria of agon similarity and similarity of inter-agon distance
matrices.
Database Mining within Moloc utilizes the second method for
computational speed reasons. It consists of the following steps:
- Conversion of the database to be searched to topological
pharmacophore format (once)
- Conversion of lead structures or 3-d pharmacophores to
pharmacophore format
- Similarity calculation between search pharmacophore and
Database pharmacophores
- 3-d structure extraction of top ranked database items
- Matching hit structures onto 3-d lead or pharmacophore
- Scanning of hit-library against background protein or target
structure or -pharmacophore
- Filtering of hit library for various pharmacokinetic properties