Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Journal of Cheminformatics

Volumn 7, Issue 1, 2015, Pages

  Bajusz, Dávid ^a   Rácz, Anita ^a,b   Héberger, Károly ^a  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b Institute of Entrepreneurship and Innovation   (Hungary)

Author keywords

Analysis of variance; Data fusion; Distance metrics; Fingerprint; Ranking; Similarity; Sum of ranking differences

Indexed keywords

EID: 84930655650     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/s13321-015-0069-3     Document Type: Article

References (40)

1. Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem. 2004;2:3204-18.
2. Maggiora G, Vogt M, Stumpfe D, Bajorath J. Molecular similarity in medicinal chemistry. J Med Chem. 2014;57:3186-204.
3. Kubinyi H. Similarity and dissimilarity: a medicinal chemist's view. Perspect Drug Discov Des. 1998;9-11:225-52.
4. Eckert H, Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today. 2007;12:225-33.
5. Bender A, Jenkins JL, Scheiber J, Sukuru SCK, Glick M, Davies JW. How similar are similarity searching methods?: a principal component analysis of molecular descriptor space. J Chem Inf Model. 2009;49:108-19.
6. Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50:742-54.
7. Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Garcia-Vallvé S, Pujadas G. Molecular fingerprint similarity search in virtual screening. Methods. 2015;71:58-63.
8. Kokare M, Chatterji BN, Biswas PK. Comparison of similarity metrics for texture image retrieval. In: Proceedings of TENCON 2003 Conference on Convergent Technologies for the Asia-Pacific Region, vol. 2. Edited by IEEE. 2003. p. 571-5.
9. Strehl A, Strehl E, Ghosh J, Mooney R. Impact of similarity measures on web-page clustering. In: Proceedings of the Workshop on Artificial Intelligence for Web Search (AAAI 2000). Edited by AAAI. 2000. p. 58-64.
10. Becker H, Naaman M, Gravano L. Learning similarity metrics for event identification in social media. In: Proceedings of the third ACM international conference on Web search and data mining. New York, NY, USA: ACM; 2010. p. 291-300. WSDM'10.
11. Reisen F, Zhang X, Gabriel D, Selzer P. Benchmarking of multivariate similarity measures for high-content screening fingerprints in phenotypic drug discovery. J Biomol Screen. 2013;18:1284-97.
12. Holliday JD, Hu C-Y, Willett P. Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment Bit-strings. Comb Chem High Throughput Screen. 2002;5:155-66.
13. Chen X, Reynolds CH. Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. J Chem Inf Comput Sci. 2002;42:1407-14.
14. Salim N, Holliday J, Willett P. Combination of fingerprint-based similarity coefficients using data fusion. J Chem Inf Comput Sci. 2003;43:435-42.
15. Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov Today. 2006;11:1046-53.
16. Todeschini R, Consonni V, Xiang H, Holliday J, Buscema M, Willett P. Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J Chem Inf Model. 2012;52:2884-901.
17. Willett P. Combination of similarity rankings using data fusion. J Chem Inf Model. 2013;53:1-10.
18. Whittle M, Gillet VJ, Willett P, Alex A, Loesel J. Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients. J Chem Inf Comput Sci. 2004;44:1840-8.
19. Flower DR. On the properties of Bit string-based measures of chemical similarity. J Chem Inf Comput Sci. 1998;38:379-86.
20. Lajiness MS. Dissimilarity-based compound selection techniques. Perspect Drug Discov Des. 1997;7/8:65-84.
21. Dixon SL, Koehler RT. The hidden component of size in two-dimensional fragment descriptors: side effects on sampling in bioactive libraries. J Med Chem. 1999;42:2887-900.
22. Holliday JD, Salim N, Whittle M, Willett P. Analysis and display of the size dependence of chemical similarity coefficients. J Chem Inf Comput Sci. 2003;43:819-28.
23. Godden JW, Xue L, Bajorath J. Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. J Chem Inf Comput Sci. 2000;40:163-6.
24. Yan X, Yu P, Han J. Substructure similarity search in graph databases. In: Proceedings of the 2005 ACM SIGMOD international conference on Management of data. Edited by ACM 2005. p. 766-77.
25. Klinger S, Austin J. Weighted superstructures for chemical similarity searching. In: Proceedings of the 9th Joint Conference on Information Sciences. 2006.
26. Héberger K, Kollár-Hunek K. Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers. J Chemom. 2011;25:151-8.
27. Héberger K. Sum of ranking differences compares methods or models fairly. TrAC Trends Anal Chem. 2010;29:101-9.
28. Kiss R, Sándor M, Szalai FA. http://Mcule.com: a public web service for drug discovery. J Cheminform. 2012;4:17.
29. KNIME | Konstanz Information Miner, University of Konstanz, Germany. 2014. [ https://www.knime.org/ ]
30. JChem 2.8.2, ChemAxon LLC, Budapest, Hungary. 2014 [ http://www.chemaxon.com ]
31. Todeschini R, Ballabio D, Consonni V, Mauri A, Pavan M. CAIMAN (classification and influence matrix analysis): a new approach to the classification based on leverage-scaled functions. Chemom Intell Lab Syst. 2007;87:3-17.
32. Willett P, Barnard J, Downs G. Chemical similarity searching. J Chem Inf Comput Sci. 1998;38:983-96.
33. Kollár-Hunek K, Héberger K. Method and model comparison by sum of ranking differences in cases of repeated observations (ties). Chemom Intell Lab Syst. 2013;127:139-46.
34. Chemical Hashed Fingerprint [ https://docs.chemaxon.com/display/CD/Chemical+Hashed+Fingerprint ].
35. RDKit: Cheminformatics and Machine Learning Software, Open-source. 2014. [ http://www.rdkit.org/ ]
36. Hastie T, Tibshirani R, Friedman J. Overview of supervised learning. In: Elements of Statistical Learning: Data Mining, Inference, and Prediction, 31. New York: Springer; 2001.
37. STATISTICA 12.5, StatSoft, Inc., Tulsa, OK 74104, USA, 2014. [ http://www.statsoft.com/Products/STATISTICA-Features/Version-12 ].
38. Carr RAE, Congreve M, Murray CW, Rees DC. Fragment-based lead discovery: leads by design. Drug Discov Today. 2005;10:987-92.
39. Teague SJ, Davis AM, Leeson PD, Oprea T. The design of leadlike combinatorial libraries. Angew Chemie Int Ed. 1999;38:3743-8.
40. Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods. 2000;44:235-49.