Similarity searching in large combinatorial chemistry spaces

Abstract

We present a novel algorithm, called Ftrees-FS, for similarity searching in large chemistry spaces based on dynamic programming. Given a query compound, the algorithm generates sets of compounds from a given chemistry space that are similar to the query. The similarity search is based on the feature tree similarity measure representing molecules by tree structures. This descriptor allows handling combinatorial chemistry spaces as a whole instead of looking at subsets of enumerated compounds. Within few minutes of computing time, the algorithm is able to find the most similar compound in very large spaces as well as sets of compounds at an arbitrary similarity level. In addition, the diversity among the generated compounds can be controlled. A set of 17 000 fragments of known drugs, generated by the RECAP procedure from the World Drug Index, was used as the search chemistry space. These fragments can be combined to more than 10¹⁸ compounds of reasonable size. For validation, known antagonists/inhibitors of several targets including dopamine D4, histamine H1, and COX2 are used as queries. Comparison of the compounds created by Ftrees-FS to other known actives demonstrates the ability of the method to jump between structurally unrelated molecule classes.