Unsupervised classification of chemical compounds

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.