Drugs and monomers were used to compute several additional properties through RDKit, representing various methods of describing substructures or the local neighborhood around individual atoms (Figure ): extended connectivity fingerprints with radii 4 and 6 (ECFP4 and ECFP6 ), RDKit fingerprints with maximum path lengths of 5 and 7 (RDK5 and RDK7), Avalon fingerprints and MACCS keys . Furthermore, ISIDA fragments (maximum length of linear and atom-centered fragments = 5) and CircuS descriptors (up to a radius of 4) were obtained through CIMTools and CGRTools , as implemented within Chem-InfoTools . As increasing the fingerprint size to reduce the amount of potential bit collisions has shown to improve performance , the standard fingerprint size was increased to 16384 for ECFP and RDK fingerprints. As all of the calculated fingerprints resemble count vectors, in order to compute mixture-specific fingerprints the counts on each bit of the relevant structures were multiplied by their respective number (number of A/B monomer in one polymer molecule and number of drug molecules per polymer chain) and subsequently summed up. At last, SMILES codes were also used to generate molar fraction-weighted mol2vec (m2v) vector representations, based on circular Morgan fingerprints with radii up to 1 . Hereby, the molecules are located in a dense, 300-dimensional space, in which similar substructures of the chemical space are located in close proximity (Figure ). These vector embeddings are based on an unsupervised machine learning procedure on the ZINC database (19.9 million compounds ) via the natural language processing algorithm word2vec .