  In the current study we examine an application of the machine learning
methods to model the retention constants in the thin layer chromatography
(TLC). This problem can be described with hundreds or even thousands of
descriptors relevant to various molecular properties, most of them redundant
and not relevant for the retention constant prediction. Hence we employed
feature selection to significantly reduce the number of attributes.
Additionally we have tested application of the bagging procedure to the feature
selection. The random forest regression models were built using selected
variables. The resulting models have better correlation with the experimental
data than the reference models obtained with linear regression. The
cross-validation confirms robustness of the models.
