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On the Analysis of Compressed Chemical Fingerprints

  • Fabio GrandiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)

Abstract

Chemical fingerprints are binary strings used to represent the distinctive features of molecules in order to efficiently support similarity search of chemical data. In large repositories, chemical fingerprints are conveniently stored in compressed format, although the lossy compression process may introduce a systematic error on similarity measures. Simple correction formulae have proposed by Swamidass and Baldi in [13] to compensate for such an error and, thus, to improve the similarity-based retrieval. Correction is based on deriving estimates for the weight (i.e., number of bits set to 1) of fingerprints before compression from their compressed values. Although the proposed correction has been substantiated by satisfactory experimental results, the way in which such estimates have been derived and the approximations applied in [13] are not fully convincing and, thus, deserve further investigation. In this direction, the contribution of this work is to provide some deeper insight on the fingerprint generation and compression process, which could constitute a more solid theoretical underpinning for the Swamidass and Baldi correction formulae.

References

  1. 1.
    Aung, Z., Ng, S.-K.: An indexing scheme for fast and accurate chemical fingerprint database searching. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 288–305. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13818-8_22CrossRefGoogle Scholar
  2. 2.
    Bohacek, R.S., McMartin, C., Guida, W.C.: The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16(1), 3–50 (1996)CrossRefGoogle Scholar
  3. 3.
    Daylight Chemical Information Systems Inc. http://www.daylight.com/
  4. 4.
    Flower, D.R.: On the properties of bit string-based measures of chemical similarity. J. Chem. Inform. Comput. Sci. 38(3), 378–386 (1998)CrossRefGoogle Scholar
  5. 5.
    Grandi, F.: Advanced access cost models for databases. Ph.D. Dissertation (in Italian), DEIS, University of Bologna, Italy (1994)Google Scholar
  6. 6.
    Grandi, F.: On the signature weight in “multiple” \(m\,\) signature files. ACM SIGIR Forum 29(1), 20–25 (1995)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Grandi, F.: The \(\gamma \)-transform approach: a new method for the study of a discrete and finite random variable. Int. J. Math. Models Methods Appl. Sci. 9, 624–635 (2015). http://www.naun.org/main/NAUN/ijmmas/2015/b442001-411.pdf
  8. 8.
    Grandi, F.: On the analysis of Bloom filters. Inf. Process. Lett. 129, 35–39 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Grandi, F., Scalas, M.R.: Block access estimation for clustered data using a finite LRU buffer. IEEE Trans. Softw. Eng. 19(7), 641–660 (1993)CrossRefGoogle Scholar
  10. 10.
    Hajiebrahimi, A., Ghasemi, Y., Sakhteman, A.: FLIP: an assisting software in structure based drug design using fingerprint of protein-ligand interaction profiles. J. Mol. Graph. Model. 78, 234–244 (2017)CrossRefGoogle Scholar
  11. 11.
    Rouvray, D.: Definition and role of similarity concepts in the chemical and physical sciences. J. Chem. Inf. Comput. Sci. 32(6), 580–586 (1992)CrossRefGoogle Scholar
  12. 12.
    Swamidass, S.J., Baldi, P.: Statistical distribution of chemical fingerprints. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds.) WILF 2005. LNCS (LNAI), vol. 3849, pp. 11–18. Springer, Heidelberg (2006).  https://doi.org/10.1007/11676935_2CrossRefGoogle Scholar
  13. 13.
    Swamidass, S.J., Baldi, P.: Mathematical correction for fingerprint similarity measures to improve chemical retrieval. J. Chem. Inf. Model. 47(3), 952–964 (2007)CrossRefGoogle Scholar
  14. 14.
    Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327–352 (1977)CrossRefGoogle Scholar
  15. 15.
    Yao, S.B.: Approximating block accesses in database organizations. Commun. ACM 20(4), 260–261 (1977)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering (DISI)Alma Mater Studiorum – Università di BolognaBolognaItaly

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