  With the proliferation of screening tools for chemical testing, it is now
possible to create vast databases of chemicals easily. However, rigorous
statistical methodologies employed to analyse these databases are in their
infancy, and further development to facilitate chemical discovery is
imperative. In this paper, we present conditional Gaussian process models to
predict ordinal outcomes from chemical experiments, where the inputs are
chemical compounds. We implement the Tanimoto distance, a metric on the
chemical space, within the covariance of the Gaussian processes to capture
correlated effects in the chemical space. A novel aspect of our model is that
the kernel contains a scaling parameter, a feature not previously examined in
the literature, that controls the strength of the correlation between elements
of the chemical space. Using molecular fingerprints, a numerical representation
of a compound's location within the chemical space, we show that accounting for
correlation amongst chemical compounds improves predictive performance over the
uncorrelated model, where effects are assumed to be independent. Moreover, we
present a genetic algorithm for the facilitation of chemical discovery and
identification of important features to the compound's efficacy. A simulation
study is conducted to demonstrate the suitability of the proposed methods. Our
proposed methods are demonstrated on a hazard classification problem of organic
solvents.
