- Poster presentation
- Open Access
Distance phenomena in high-dimensional chemical descriptor spaces: consequences for similarity-based approaches
https://doi.org/10.1186/1752-153X-3-S1-P28
© Rupp and Schneider; licensee BioMed Central Ltd. 2009
- Published: 05 June 2009
Keywords
- Vector Space
- Similarity Measure
- Similarity Coefficient
- Mathematical Concept
- Empty Space
Measuring the (dis)similarity of molecules is, besides descriptor selection, an important factor for many cheminformatics applications like compound ranking, clustering, and, property prediction. In this work, we focus on real-valued vector spaces (as opposed to the binary spaces of, e.g., fingerprints). We demonstrate the severe influence the choice of (dis)similarity measure can have on the results of cheminformatics applications, and provide recommendations for such choices.
We briefly review the mathematical concepts [1] used to measure (dis)similarity in vector spaces, namely norms, metrics, inner products and similarity coefficients, and the relationships between them, employing commonly used [2][3] (dis)similarity measures in cheminformatics as examples.
Then, we present several phenomena (empty space phenomenon, sphere volume related phenomena, distance concentration [4][5][6]) in high-dimensional descriptor spaces which are not encountered in two and three dimensions. These phenomena are theoretically characterized and illustrated with both artificial and real (bioactivity) data examples.
Authors’ Affiliations
References
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Copyright
This article is published under license to BioMed Central Ltd.
