Unsupervised classification of chemical compounds. (English) Zbl 0923.62107
Summary: 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.
MSC:
62N99 | Survival analysis and censored data |
62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
62P99 | Applications of statistics |
92C40 | Biochemistry, molecular biology |
92E10 | Molecular structure (graph-theoretic methods, methods of differential topology, etc.) |