  Machine learning, data mining and artificial intelligence (AI) based methods
have been used to determine the relations between chemical structure and
biological activity, called quantitative structure activity relationships
(QSARs) for the compounds. Pre-processing of the dataset, which includes the
mapping from a large number of molecular descriptors in the original high
dimensional space to a small number of components in the lower dimensional
space while retaining the features of the original data, is the first step in
this process. A common practice is to use a mapping method for a dataset
without prior analysis. This pre-analysis has been stressed in our work by
applying it to two important classes of QSAR prediction problems: drug design
(predicting anti-HIV-1 activity) and predictive toxicology (estimating
hepatocarcinogenicity of chemicals). We apply one linear and two nonlinear
mapping methods on each of the datasets. Based on this analysis, we conclude
the nature of the inherent relationships between the elements of each dataset,
and hence, the mapping method best suited for it. We also show that proper
preprocessing can help us in choosing the right feature extraction tool as well
as give an insight about the type of classifier pertinent for the given
problem.
