  The paper presents an application of Conformal Predictors to a
chemoinformatics problem of identifying activities of chemical compounds. The
paper addresses some specific challenges of this domain: a large number of
compounds (training examples), high-dimensionality of feature space, sparseness
and a strong class imbalance. A variant of conformal predictors called
Inductive Mondrian Conformal Predictor is applied to deal with these
challenges. Results are presented for several non-conformity measures (NCM)
extracted from underlying algorithms and different kernels. A number of
performance measures are used in order to demonstrate the flexibility of
Inductive Mondrian Conformal Predictors in dealing with such a complex set of
data.
  Keywords: Conformal Prediction, Confidence Estimation, Chemoinformatics,
Non-Conformity Measure.
