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ccbmlib – a Python package for modeling Tanimoto similarity value distributions

Authors
  • Vogt, Martin1
  • Bajorath, Jürgen1
  • 1 Department of Life Science Informatics, B-IT, University of Bonn, Endenicher Allee 19c, Bonn, NRW, 53115, Germany
Type
Published Article
Journal
F1000Research
Publisher
"F1000 Research, Ltd."
Publication Date
Mar 05, 2020
Volume
9
Identifiers
DOI: 10.12688/f1000research.22292.2
PMID: 32161645
PMCID: PMC7050271
Source
PubMed Central
Keywords
License
Unknown

Abstract

The ccbmlib Python package is a collection of modules for modeling similarity value distributions based on Tanimoto coefficients for fingerprints available in RDKit. It can be used to assess the statistical significance of Tanimoto coefficients and evaluate how molecular similarity is reflected when different fingerprint representations are used. Significance measures derived from p -values allow a quantitative comparison of similarity scores obtained from different fingerprint representations that might have very different value ranges. Furthermore, the package models conditional distributions of similarity coefficients for a given reference compound. The conditional significance score estimates where a test compound would be ranked in a similarity search. The models are based on the statistical analysis of feature distributions and feature correlations of fingerprints of a reference database. The resulting models have been evaluated for 11 RDKit fingerprints, taking a collection of ChEMBL compounds as a reference data set. For most fingerprints, highly accurate models were obtained, with differences of 1% or less for Tanimoto coefficients indicating high similarity.

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