While all these approaches offer distinct advantages and are subject to certain limitation, we argue that a fragmentation approach for the calculation of protein-ligand interactions should ideally have the following features: First, it should employ chemically meaningful fragments, such as single amino acids. Not only does this enhance the interpretability of the results and allows for insightful analysis , but it will also provide an ideal staring point for the parametrization of machine-learning potentials . Here, the Molecular Fractionation with Conjugate Caps (MFCC) scheme , which cuts the protein at the peptide bonds into single amino acid fragments and restores meaningful fragments by capping the severed bonds with acetyl groups (ACE) and N -methylamide groups (NME), is particularly attractive (see Sect. 2.1). Second, it should provide the possibility for systematic improvement, both in terms of the underlying quantum-chemical methods and in terms of the fragmentation scheme. For the latter, approaches based on a (generalized) many-body expansion (MBE) allow for systematically reducing the fragmentation error by including higher-order contributions. Third, it should not only provide access to total energies, but also to other molecular properties, such as the electron density. This will facilitate the inclusion of density-based energy corrections on top of a MBE, as demonstrated in our previous work for molecular clusters . While simpler capping schemes are often sufficient for obtaining accurate energies , the MFCC partitioning has been shown to also provide access to accurate electron densities .