In order to further evaluate the proper ranking of the four compounds (DB, CDB, DDB and HDB), we applied a second completely independent affinity prediction method based on machine learning. The method is described in details in a previous publication . Training of the random forest machine learning algorithm in regression mode was performed based on the ligand dataset BRAF-V600E (with annotated IC50 affinity measures -2193 molecules) from BindingDB (2018). The method relies on data from multi-structure docking and pose evaluation of the ligand dataset on the @TOME server taking into account all available BRAF structures, and ligand-based molecular descriptors. The machine learning method predicted the following affinity ranking (from highest to lowest) with pIC50 values ([-log10(M )]): Remarkably, the machine learning based ranking is the same as for the DSX evaluation, also predicting highly similar binding affinities for the metabolites and an increased affinity for DB. This again, confirms the validity of the choice of the dielectric constant ( =8), at with this tendency is equivalently reproduced.