We investigate to what extent yeast complementation assays, which in principle can provide large amounts of training data for machine learning models, yield quantitative correlations between growth rescue and single channel recordings. If this were the case, yeast complementation results could be used as surrogate data for machine learning-based channel design. Therefore, we mutated position L94 at the cavity entry of the model K