Unsupervised Classification of Chemical Compounds
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.