Study of diversity and similarity of large chemical databases using tanimoto measure

Abstract

ZINC is a freely available chemical database which contains 27 million compounds including Drug-like, Natural Products, FDA etc., along with 9 molecular features. In this paper firstly we compute an additional number of 49 molecular features and represent the entire chemical space in the 58-length finger print space. Tanimoto metric, a popular similarity measure is used to mine the chemical space for extracting similar and diverse fingerprints. One of the important issues is that of choosing a proper reference string. Experiments with different reference strings are carried out to assess the appropriateness of a reference string. A finger print which is constituted by mandating non-trivial presence of each feature is found to be the best. Further a method which is independent of reference string is proposed using pairwise distribution but this raises the time complexity from linear to quadratic. A subgoal of this paper is also to propose a scheme that extracts a small sample data set that reflects the similarity and diversity of the population. Towards this, we conduct stratified sampling of Natural Products Database(NPD) which has 90,000 chemical compounds by dividing the space along strata representing distinct structures (rings) and then compute pairwise similarity profile. This scheme can be extended to other data bases that reside in ZINC.