Prima facie, the ever-increasing chemical diversity of sophisticated nanotechnologies theoretically available for drug formulation should provide researchers with a suitable arsenal to overcome most solubility issues. In practice, due to lack of mechanistic insight into the driving forces within such DDS, formulation development is still driven mainly heuristically by time-and resource-intensive experimental screenings; this has resulted in only very limited cases of alternative delivery systems to be used in marketed therapeutics . It is therefore of great interest for the pharmaceutical research community to optimize development processes via integration of complementary in silico methods. High-throughput virtual screenings employing techniques like molecular docking are already well-established during the phase of early drug design . In recent years, increasing amounts of computational resources and available experimental data have also allowed molecular dynamics simulations , machine learning or quantitative structure-property relationship (QSPR) modeling to guide researchers during the later stage of drug formulation. Thus, the term "computational pharmaceutics" has emerged to describe the usage of in silico methods for the purpose of optimizing pharmaceutical technologies and drug delivery processes . While QSPR modeling is traditionally performed by correlation of small molecule descriptors to physicochemical or pharmacological properties , its usage has expanded to other fields including studies on mixtures of compounds , predicting chemical reactions , or characterization of various polymer properties . In this regard, "polymer informatics" represents a rather new but active field of research and various types of representations of macromolecular structures for QSPR modeling and machine learning pipelines have been described, including (Big)SMILES strings , classical descriptors, fingerprints and binary images , or, more recently, methods inspired by natural language processing .