Name
Title | ||
|---|---|---|
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? |
Abstract | ||
|---|---|---|
This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics. Graphical AbstractA visual summary of the comparison of similarity metrics with sum of ranking differences (SRD). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s13321-015-0069-3 | Journal of Cheminformatics |
Keywords | Field | DocType |
Analysis of variance,Data fusion,Distance metrics,Fingerprint,Ranking,Similarity,Sum of ranking differences | Data mining,Ranking,Computer science,Toolbox,Similarity (network science),Fingerprint,Sensor fusion,Bioinformatics | Journal |
Volume | Issue | ISSN |
7 | 1 | 1758-2946 |
Citations | PageRank | References |
42 | 1.61 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dávid Bajusz | 1 | 42 | 1.61 |
| Anita Rácz | 2 | 42 | 1.61 |
| Károly Héberger | 3 | 51 | 6.76 |