Reviews in Computational Chemistry Volume 18

28 Clustering Methods and Their Uses in Computational Chemistry
is the OPTICS method that orders items in a data set in terms of local criteria,
thus providing an equivalent to a density-based analysis.
A variety of different requirements exist for chemical applications. These
requirements dictate whether it is important to address the issues of how many
clusters exist, what the best partition is, and which the best clusters are. When
using representative sampling, for example, for high-throughput screening in
pharmaceutical research, the number of required clusters is usually set beforehand.
Hence, it is necessary to generate only a reasonable partition from which
to extract the required number of representative compounds. For analysis of
an unknown data set in, say, a list of vendor compounds, the number of clusters
is unknown. Hierarchical clustering with optimum level analysis should
provide suitable results for this scenario since the actual composition of
each cluster is not critical. For analysis of quantitative structure–activity relationships
(QSAR), the number of clusters is unknown, and the quality of the
clusters becomes an important issue since complete clusters are required for
further analysis. It may be that recent developments 87–93 related to densitybased
clustering will help in this circumstance.
CHEMICAL APPLICATIONS
Having introduced and described the various kinds of clustering methods
used in chemistry and other disciplines, we are in a position to present some
illustrative examples of chemical applications. This section reviews a representative
selection of publications that have reported or analyzed the use of clustering
methods for processing chemical data sets, largely from groups of
scientists working within pharmaceutical companies. The main applications
for these scientists are high-throughput screening, combinatorial chemistry,
compound acquisition, and QSAR. The emphasis is on pharmaceutical applications
because these workers tend to process very large and high dimensional
data sets. This section is presented according to method, starting with hierarchical
and then moving to nonhierarchical methods.
Little has been reported on the use of hierarchical divisive methods for
processing chemical data sets (other than the inclusion of the minimumdiameter
method in some of the comparative studies mentioned above).
Recursive partitioning, which is a supervised classification technique very
closely related to monothetic divisive clustering, has, however, been used at
the GlaxoSmithKline 57,58 and Organon 59 companies.
There is, however, widespread use of hierarchical agglomerative techniques,
particularly the Ward method. At Organon, Bayada, Hamersma,
and van Geerestein 121 compared Ward clustering with the MaxMin diversity
selection method, Kohonen maps, and a simple partitioning method to help
select diverse yet representative subsets of compounds for further testing.
The data came from HTS or combinatorial library results. Ward clustering