Polymeric micelles represent one of the many delivery strategies that has garnered much attention but limited translation . For example, within the last decade amphiphilic ABA-triblock copolymers consisting of poly(2-oxazoline) (pOx) and poly(2-oxazine) (pOzi) monomers with readily-tunable sidechains showed great value as carrier for hydrophobic drugs with therapeutic value, e.g. anticancer agents like paclitaxel . In presence of drugs, these polymers can form micelles where the hydrophilic A blocks form a protective "corona" around the inner hydrophobic B blocks as main drug carrier (Figure ). However, this image is too simplistic as A blocks have shown to also interact with drugs, questioning the widely established but rather simplistic picture of a core-shell architecture . Poly(2-methyl-2oxazoline) (pMeOx) is usually used as A blocks, whereas for B blocks sidechains of a length of three to four carbon atoms, including poly(2-butyl-2-oxazoline) (pBuOx), poly(2-butyl-2-oxazine) (pBuOzi), poly(2-propyl-2-oxazoline) (pPrOx) and poly(2propyl-2-oxazine) (pPrOzi), have shown to provide high drug loading for a wide variety of drugs. Branched, cyclic and linear sidechains (n-propyl/butyl variants termed nPrOx, nPrOzi, nBuOx, nBuOzi) have been tested. Of note, recent studies demonstrated a high dependence of the maximum loading capacity (LC) and loading efficiency (LE) of such micelles on both the structure of the polymer and the drug , where an exchange of B blocks with structural isomers resulted in drastic changes of the mentioned properties. Superior drug loadings for B blocks with the aforementioned, relatively short B block sidechains underline that simply increasing hydrophobicity of the inner polymer blocks does not necessarily improve drug uptake. Assessing polymer-drug compatibility through solubility parameters obtained by group contribution methods have repeatedly provided only very limited predictability . Furthermore, for the case of co-formulations containing multiple drugs, synergistic and antagonistic effects have been reported . While dissecting the driving polymer-drug interactions for explaining mechanistically the observed differences in LC and LE remains subject to future studies, a recent chemoinformatics-driven study by Alves et al. demonstrated the potential of applying QSPR modeling techniques to predict micelle drug loading. Using so-called SiRMS descriptors (simplex representation of molecular structure) designed to capture the chemical nature of polymer-drug mixtures by defining smaller fragments of the molecules , they generated prediction models that were sucessfully used to improve the experimental hit rate of finding new formulations with high drug loading, as was determined on a subset of eight selected hit compounds from virtual screening. Until now, multiple new studies with additional formulation data regarding these DDS have been published, including a variety of drugs, polymer compositions and information on long-term stability to evaluate shelf-life. Thus, in this work we aim to collect the experimental data of previous publications, combined with in-house data, in order to generate, with a significantly extended dataset, a prediction tool that can readily be used by other formulation scientists to evaluate whether these types of polymers could provide an enhanced solubility profile for a potential new drug candidate. Virtual screening of known compounds further provides an openly accessible database of potential use cases with regard to already known, poorly-soluble drugs.