In order to describe polymer-drug mixtures, various molecular features were considered as input variables for model building (Figure ). Similar to the previous study of Alves et al. , in order to describe mixtures within a QSPR modeling framework, SiRMS descriptors were generated. This technique was developed by Kuz'min et al. and was successfully used for a variety of QSPR modeling approaches including mixtures and reactions . Hereby, all possible tetraatomic subgraphs (called simplexes) describing the chemical nature of smaller unbound and bound fragments of the relevant molecules within a mixture are counted; these numbers represent the simplex descriptor values for subsequent model building (Figure ). For generation of simplexes, different properties were taken into account during clustering of atoms into different subgroups A-D. The HiT QSAR software previously used by Alves et al. is based on a graphical interface with slow calculation speed and requires a ChemAxon license to calculate certain atomic properties. In order to develop an open-source tool designed for large-scale screening purposes, we utilized the SiRMS.py tool that was also used in a recent study of Rakhimbekova et al. for these polymers. Lipophilicity (logp: A ≤ -0.5 < B ≤ 0 < C ≤ 0.5 < D) and refraction (mr: A ≤ 1.5 < B ≤ 3 < C ≤ 8 < D) were calculated based on Crippen's approach implemented in RDKit 2022.9.5 . Clustering for these properties was performed with the same thresholds used by Alves et al. . In analogy to this study, SMILES codes of drugs and small pseudotrimers, comprising each monomeric building block and termini once, were used for generation of SiRMS descriptors. As only tetraatomic simplexes are counted within this approach, the usage of pseudotrimers, with the molar ratios of the actual full-length polymer-drug complexes, captures all relevant fragments, but also assumes similar monomer ratios (A:B monomers ≈ 2:1). Atom number labelings were included in combination with hybridization states (e.g. the labeling 6>sp² in our SiRMS descriptor set represents a sp²-hybridized carbon atom), determined via the recently published software tool Jazzy 0.0.11 which itself is built on kallisto 1.0.9 . This tool was further used to compute an extended set of atomic properties: electronegativity equilibration charges (eeq: A ≤ -0.28 < B ≤ 0 < C ≤ 0.28 < D, corresponding to cutoffs used by ISIDA ), atomic-charge dependent dynamic atomic polarizabilities (alp: A ≤ 6 < B ≤ 9 < C ≤ 12 < D), as well as chargedependent hydrogen bond acceptor and donor strengths (sa, sdx and sdc: A ≤ 0.5 < B ≤ 0.75 < C ≤ 1 < D), where a value of 1 corresponds to the strength of a water H-bond. The SiRMS descriptor set was calculated with and without (SiRMS-noH) consideration of explicit hydrogen atoms.