2 Miron B. Kursa, Lukasz Komsta, and Witold R. Rudnicki system. The quantitative structure-retention relationship (QSRR) investigations are widely established and often used in prediction of a retention for new solutes, finding the most informative structure descriptors for retention explaining and checking their compliance with the molecular theory of the separation [4,3].

The QSRR investigations regarding to the thin layer chromatography resulted in many equations able to predict the retention for only some groups of analogs. Most of them concern the retention in reversed-phase systems, where retention is strictly correlated with the lipophilicity of the solute [11,13,2]. On the other hand the QSRR models useful for prediction of the retention on silica gel
(where the retention mechanism is much more complicated) are relatively scarce. Recently several models on atomic contributions [5], and substituent groups [6,7] were proposed by Komsta, These models have reasonable predictive abilities for most studied systems.

Nevertheless, there are several problems with general linear models that limit their predictive power in some difficult cases. The molecular systems are often described with hundreds, or even thousands of various descriptors, whereas the experimental data on retention for TLC systems is limited to hundreds of molecules at best. The standard statistical methods are developed for systems where the number of data points is at least one order of magnitude higher than number of descriptive variables. Furthermore, they don't work well for systems with large number of variables that may also be collinear. The linear models are developed using an a priori selected set of variables that are only a small subset of all descriptors that can be used to describe the system. However, such selection, even performed by the expert, is arbitrary and not necessarily optimal.

An alternative approach for modelling large experimental data sets with unknown relationships between descriptive variables and response variable has been developed by the machine learning community. Machine learning is in principle similar to statistical modelling, yet the generality is achieved by optimising the training process so that the model will work well on previously unseen data rather than constraining the number of model's degrees of freedom based on some assumptions.

The current study is devoted to exploring application of the random forest
(RF) algorithm [1] for modelling retention constants in thin layer chromatography. To this end we selected two examples from the systems studied previously by Komsta [6], and performed extensive tests of the methodology. We show that application of the random forest for modelling retention in TLC systems gives models that are robust, have better accuracy than their linear counterparts.

Moreover, the feature selection algorithm that was applied to reduce noise, have also chosen variables that make sense from the chemists point of view.

## 2 Materials And Methods

The data for retention coefficients in two TLC systems: Chloroform:methanol (90:10) (TAD) and Chloroform:cyclohexane:acetic acid (4:4:2) (TAK) were used. The data sets comprised coefficients for 225 and 257 compounds in the case of