Empirical methods tend to benefit from a large and diverse training set, especially when there's a large body of experimental data to train on, such as octanol-water data like in the present and previous log P challenge . However, empirical methods can experience problems if a training set has an underrepresented functional group. Additionally, these techniques are geared towards partitioning predictions, and, unlike physical-based methods, are not able to be applied to protein-ligand binding.