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Unsupervised Classification of Chemical Compounds

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  • P. Guttiérrez Toscano
  • F. H. C. Marriott

Abstract

Clustering chemical compounds of similar structure is important in the pharmaceutical industry. One way of describing the structure is the chemical `fingerprint'. The fingerprint is a string of binary digits, and typical data sets consist of very large numbers of fingerprints; a suitable clustering procedure must take account of the properties of this method of coding, and must be able to handle large data sets. This paper describes the analysis of a set of fingerprint data. The analysis was based on an appropriate distance measure derived from the fingerprints, followed by metric scaling into a low‐dimensional space. An approximation to metric scaling, suitable for very large data sets, was investigated. Cluster analysis using two programs, mclust and AutoClass‐C, was carried out on the scaled data.

Suggested Citation

  • P. Guttiérrez Toscano & F. H. C. Marriott, 1999. "Unsupervised Classification of Chemical Compounds," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(2), pages 153-163.
    • Handle: RePEc:bla:jorssc:v:48:y:1999:i:2:p:153-163
    DOI: 10.1111/1467-9876.00146
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