We developed several R and Python scripts to perform exploratory analyses, to compare the generated ML classifications and to analyze each model in depth. The algorithms used in the classifications were simple Partition Tree (PT), Random Forest (RF), and XGBoost (XGB) algorithms. The ErG columns (descriptors/bits) showing variance lower than 0.2 were removed to generate a matrix of 662 rows X 123 columns, where rows are ligands collected and the columns are ErG bits remaining after variance filtration. We assumed that lower or constant variance columns should not contribute to final models (). We generated several models with different classification algorithms and automatically selected the best algorithm on the basis of highest Cohen's kappa value () defined in here below: