The datasets we used so far had to be filtered to only include molecules not exceeding a certain number of non-hydrogen atoms. This was necessary since Myerson value calculations scale exponentially with the number of players (atoms) in the graph (molecule). As a result the computational complexity of the Myerson value calculation becomes infeasible. Moreover, the collection of subgraphs also increases exponentially so it can not be stored in memory anymore (Figure ). To be able to explain predictions on larger molecules we implemented Monte-Carlo sampling of player coalitions following a permutations sampling approach from Tarkowski et al. described above. Monte Carlo sampling allows to adjust a trade-off between precision and computation time by the number of sampling steps. We tested different numbers of sampling steps and found the number of 1000 was a good compromise between accuracy and calculation time for molecules as large as 50 heavy atoms (Figure and Supporting Information).